esa-sceva/satcom-chunk-collection huggingface dataset:
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Combinational Considerations such as Fragmentation and Duplication  
To compute an overall QoS value of a network composed of multi-hop and parallel logical channels, it is necessary to consider the effects of the fragmentation and duplication of messages on the QoS metrics.  
Consider a simple point-to-point network with one channel and perfect, unbreakable encryption. What if that system were to instead have two parallel channels, one with perfect encryption, and the other with no encryption? How should the summary QoS values then be computed? If the data were fragmented into equally-sized portions, and an adversarial listener has access to both of those channels, then they would have unencrypted access to exactly half of the data. However, equal sized portions is not the only way to split the data. A \"single fragment could represent a single bit of a message while the maximally-sized fragment could contain the entire message\" [23]. Therefore, the amount of data accessible to an adversary is based upon the proportions of data that are transmitted across the two parallel channels, and would be represented as a weighted average.  
The previous example assumed that the message was split evenly and without any duplication. If only a single copy of each bit from a given message is sent, either physically via multiple channels or temporally via repeated messages, then there is zero duplication. Conversely, \"full duplication indicates that the entire message is sent across each of the available channels\" [23], which would lead, in the previous example, to the adversary having full access to the message via the unencrypted channel.  
If the data is unevenly or non-uniformly split among the channels, in a similar manner to RAID architectures [5], or data are duplicated across the channels, then the amount of data accessible to the adversary would be a weighted average based upon the proportional splitting and the \(DF\) of the channel(s), which in turn causes the broader QoS metrics to utilize a weighted average across the available channels.  
To maintain simplicity in the example cases, data is assumed to be split equally and without duplication across parallel links. However, the model can support more elaborate combinations when needed.
# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 4 Discussion
Numerous studies have shown that EEG signals can exhibit substantial intra-individual variability across days, devices, or mental states, which may affect the replicability of model outputs [26, 27, 28]. In our study, this challenge also appears due to data privacy constraints. Because the data used in our decentralised learning framework originate from independent local sources and are subject to anonymisation, we cannot identify whether two recordings originate from the same patient across different sessions or institutions. As such, we cannot explicitly evaluate how likely it is that an AI model would assign the same diagnosis to two EEGs recorded on separate days from the same individual. This limitation reflects a broader issue in privacy-preserving medical AI, where the need to protect patient identity may restrict the evaluation of temporal reliability. Nevertheless, the proposed dynamic ensemble strategy may help address some of these challenges. By training multiple local models on independently sourced EEG subsets and forming coalitions based on inter-model agreement or conflict, the approach aims to reduce dependence on any single, potentially noisy signal. This instance-specific selection mechanism may contribute to improved resilience against feature noise or anomalies in individual recordings. In summary, while further refinement and validation in real-world settings are needed, the results suggest that this method could provide a useful foundation for developing distributed and adaptable EEG classification systems.
## 4 Research on the Influence of Track Structure Stiffness Change on Track Deformation
### Lateral Stiffness under Rail  
According to the calculation results of the simulation conditions of different lateral stiffness under rail, the maximum values of the vertical displacement of the rail, the lateral displacement of the rail, and the gauge expansion are summarized in Table 9, the changes are shown in Figure 11.  
Figure 11: Variations in the deformation coefficients under different lateral stiffness under rail. (**a**) Wheel-rail vertical displacement change: left-time history waveform, right-maximum, (**b**) Wheel-rail lateral displacement change: left-time history waveform, right-maximum and (**c**) Change in gauge expansion: left-time history curve, right-statistical distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Vertical stiffness under rail (kN/mm) & 80 & 100 & 120 & 140 & 160 & 180 & 200 & 220 & 240 \\ Vertical displacement (mm) & 1.360 & 1.370 & 1.360 & 1.360 & 1.360 & 1.360 & 1.370 & 1.370 & 1.370 \\ Lateral displacement (mm) & 0.984 & 1.010 & 1.030 & 1.000 & 0.952 & 0.944 & 0.920 & 0.957 & 0.938 \\ Gauge expansion (mm) & 1.740 & 1.770 & 1.790 & 1.760 & 1.690 & 1.670 & 1.620 & 1.670 & 1.650 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary table of maximum values of track structure deformation control coefficients.  
According to Table 9 and Figure 11, when the lateral support stiffness under the rail is increased from 80 kN/mm to 240 kN/mm, the vertical displacement of the rail changes less, and the lateral displacement and gauge expansion are reduced accordingly. The range of amplitude changes are 0.92\(\sim\)1.032 mm, 1.625\(\sim\)1.788 mm. The relative change rates are 12.16% and 10.02%. The lateral and vertical displacement of the rail and the amplitude of the gauge expansion are all within the specified limits. In order to avoid overturning the steel rail during the running of the heavy-haul train and maintain the smoothness of the gauge, the rigidity of the lateral support under the rail can be appropriately increased. However, it should be noted that the effect of this measure will gradually weaken as the stiffness increases.
## 3 Proposed Framework (Fed-DTB)
### Trust Management System
#### 3.1.1 Trust Score Application
Collected through standard vehicle human-machine interfaces (touchscreens, voice commands, mobile applications) commonly available in contemporary vehicles. This comprehensive approach ensures that trust evaluation captures both technical performance and behavioural reliability across multiple operational dimensions while ensuring immediate deployability in existing connected vehicle fleets without requiring additional hardware investments or infrastructure modifications. The trust score computation model described in Section 2.3.1 (Equation (1)) is applied here to evaluate and select trustworthy vehicles for participation in the federated learning process. Detailed Factor Calculations  
The trust score for each factor is computed using specific metrics that measure the vehicle's performance in that factor. These factors include the IDS alert score \(\tau_{I_{i}}(t)\), network status score \(\tau_{N_{i}}(t)\), vehicle status score \(\tau_{V_{S_{i}}}(t)\), energy efficiency score \(\tau_{E_{i}}(t)\), compliance score \(\tau_{C_{i}}(t)\), federated learning participation score \(\tau_{P_{i}}(t)\), and user feedback score \(\tau_{U_{i}}(t)\)[19; 45]. In the following, we provide detailed explanations for calculating each factor score. **IDS Alert Score**  
The IDS alert score \(\tau_{I_{i}}(t)\) indicates the level of trustworthiness of the vehicle \(v_{i}\) based on the alerts provided by the IDS after analysing in-vehicle network traffic and identifying abnormalities and possible attacks. A high IDS alert score implies that the vehicle has not exhibited any malicious behaviour or been compromised. We define  
\[I_{i}(t)=\sum_{k=1}^{n_{I_{i}}}W_{k}^{\prime}\cdot I_{ik}(t) \tag{11}\]  
where  
* \(I_{ik}(t)\in\{0,1\}\) indicates whether an alert of type \(k\) is generated for vehicle \(v_{i}\) at time \(t\). * \(W_{k}^{\prime}\) is the weight of alert type \(k\), reflecting its severity and importance. * \(n_{I}\) is the number of alert types. The IDS alert score is computed as  
\[\tau_{I_{i}}(t)= \begin{cases}1,&\text{if }I_{i}(t)\leq\epsilon_{I}\\ 0,&\text{otherwise}\end{cases}  \tag{12}\]  
where \(\epsilon_{I}>0\) is a predefined IDS alert score threshold. Unlike traditional binary IDS-based exclusion mechanisms, our IDS component provides a continuous probabilistic threat score. While Equation (12) applies a threshold for exclusion in extreme cases, the underlying IDS analysis outputs a risk likelihood score, which can also be integrated into the trust computation as a continuous value. This supports more nuanced trust modulation and allows the trust framework to react proportionally to different classes and severities of detected anomalies. _Impact on Trust Score:_ If the vehicle triggers IDS alerts exceeding the threshold \(\epsilon_{I}\), it is treated as compromised and excluded by setting its trust score to zero. Otherwise, its contribution is weighted based on the trust score, which indirectly includes the probabilistic confidence output of the IDS.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
###### Abstract  
With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.  
load-balancing; back-pressure routing; LEO satellite network; routing protocol; hops-count +
Footnote †: journal: Sensors
## 4 Discussion
### Key Factors of Controlling SOS/EOS for the Whole Study Region  
In this study, we found that SOS was significantly and negatively correlated with pre-SOS precipitation and temperature in most places within the study region. This means that a warmer and wetter pre-SOS period both could cause advanced plant growth. However, considerably more pixels showed significant correlations between SOS and pre-SOS precipitation than that between SOS and pre-SOS temperature. This suggests that water availability rather than temperature mainly controls the timing of vegetation green-up in most parts of Inner Mongolian grasslands. This is, to some extent, in line with the results of two similar studies based on satellite-derived green-up date and ground meteorological data [47; 63]. Some process-based phenology modelling and experimental studies have also previously highlighted the critical effects of water availability on SOS in North American grasslands [11; 18] and Inner Mongolian grasslands [64; 65]. In contrast, however, temperature has been reported as key factor of determining spring phenological events of woody plants in temperate ecosystems [66], and herbaceous species in alpine ecosystems [67].  
Similarly, the larger number of pixels showing significant positive correlations between EOS and pre-EOS temperature than that between EOS and pre-EOS precipitation, indicates that temperature may be the more important factor than precipitation in regulating vegetation autumn phenology in temperate grasslands. However, although temperature was the dominant driver, precipitation was also positively related with EOS. Overall, warmer and wetter autumn conditions therefore would lead to a delay of the leaf senescence process. The predominant role of temperature in affecting EOS has also been observed in Mongolian grasslands [68] and in the Tibetan Plateau [69]. However, other studies reported precipitation to be more important than temperature in determining autumn phenology of temperate grassland ecosystems [47; 62]. The discrepant conclusion among different studies may be related to differences in data sample size and acquisition methods, as well as in spatial and temporal scales, but also in the studied vegetation types.
## 2 Problem Description and Assumptions
### Problem Description  
Before beginning the description of the conceptual diagram presented in Figure 1, we first state that the proposed production system comprised an unreliable production unit that was prone to random failures and repairs and produced a single product. Assuming that the machine was subject to random failures and progressive deterioration had a strong impact on the rate of defects, defining a quality deterioration phenomenon as shown in Figure 1. Minimal repairs were available to the decision maker to overcome sudden failures. However, the level of deterioration of the machine was only rejuvenated with major maintenance, which returned the rate of defects to the initial conditions. The manufacturing system used inventory as protection against the shortages caused by the numerous breakdowns of the machine. This inventory level was a decision variable of the model that must be optimized. Another available strategy in the case of high levels of deterioration was subcontracting, which satisfied a fraction of the demand. It was assumed that the manufacturing system was not capable of satisfying product demand at high levels of deterioration. Furthermore, it was conjectured that the subcontractor was unreliable because it had several companies to serve simultaneously, and also because it was subject to technical problems that limited its production responsiveness. Nevertheless, the minimum amount of availability that the subcontractor must comply with was stipulated in a contract. The advantage of subcontracting is that it provides units free of defects. In this stochastic context, the main objective of the study was to develop an innovative integral model that simultaneously determined the optimal production-subcontracting plan and major maintenance strategies that minimized the total cost in the case of quality deterioration and unreliable subcontracting. In such an optimization, several costs were considered, such as inventory, backlog, defects, major maintenance, minimal repair, and subcontracting costs. The configuration of the whole production system is presented in Figure 1.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 2 Related Work
### Research Status of LLMs and Multi-Agent System
[37] explored the application of multi-agent systems in autonomous driving technology for open-pit mines. [PERSON] et al. [38] utilized LLMs for coal mining question-and-answer systems, while [PERSON] et al. [39] developed a coal mine safety information query system based on an LLM-based multi-agent system. Moreover, [PERSON] et al. [40] developed a multi-agent system model (XCoalChat) for coal mine equipment maintenance. These studies suggest that LLM-based multi-agent systems hold significant potential in the realm of coal mine safety management, enhancing the efficiency of information retrieval, supporting decision-making processes, and advancing intelligent mining technologies. However, despite some progress in the application of LLM-based multi-agent systems in coal mine safety management, these applications remain largely limited to specific scenarios and tasks, lacking comprehensiveness and depth. For instance, current assessment systems often rely on simple rule-based judgments and lack a deep understanding and advanced analytical capabilities for complex safety issues, resulting in low accuracy and efficiency when addressing intricate safety problems. In addition, the current applications of LLMs in automated report generation are still inadequate, often relying on manual writing. This not only affects work efficiency but may also introduce subjective errors. In light of this, this research will design a task orchestration algorithm suitable for multi-agent systems, achieving an organic integration of the central controller and local agent negotiations to enhance the efficiency and flexibility of task allocation. At the same time, by integrating LLM and other advanced technologies, a gas status assessment system will be constructed for mine safety production management. This system will feature intelligent question-and-answer capabilities, gas alarm assessment, and automatic report-generation functions. It will not only understand natural language queries and provide accurate answers to enhance user experience but also analyze gas concentration levels in depth to provide early warnings and decision support. Additionally, it will automatically extract key information to generate structured reports, significantly reducing the need for human intervention, improving the efficiency and accuracy of report generation, and enhancing the level of intelligence in mine safety production management.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Evaluation with Real Datasets  
Unlike simulation-based evaluations, the environmental changes, which affect the performance of the proposed method, become complicated when operating in real environments. To test the proposed algorithms in such systems, operational traces from the initial system deployed at the hospital are collected. This dataset consisted of the reports written by the nurses and the log data generated by the system for a two-week duration. Nurses in the emergency room counted the number of mobile assets outside and inside the detection region three times a day. The nurses also compared their manual counts with the system and check if the two results differ. If the results do differ, the nurses record the numbers and the names of the assets with incorrect statuses in their reports. Of the 14-day experimental period, four different days with the differences in the nurses data and system data were observed. The data from these days were selected as the experimental data for the real dataset-based evaluations.  
The performance of the proposed method is compared to those of two other previously proposed schemes: the observation of heartbeat messages and the estimation of battery lifetime [19]. The observation that is similar to the method proposed in [17] is used as the baseline approach. An anchor node in the observation monitors heartbeat messages that a mobile node sends periodically. The anchor node is one of the neighbors of the mobile node. If an anchor node does not receive the message within 3 min from a mobile node, it sends a message indicating that the \"mobile node leaves\" the gateway. The gateway considers the mobile node to have left the detection region if it does not receive a message indicating a network-joining message within 2 min from any anchor node in the network. The estimation of battery lifetime estimates whether a mobile node has low battery power. This method considers a mobile node as a failed node if the node with a low battery level is disconnected from the WSN. To evaluate the performance levels of these methods, the number of inside and outside nodes and their neighbor counts are extracted from the log data, which the system generated on the two aforementioned dates. In addition to the log data, the records containing the periods that indicate the times to replace the battery are collected. With these data, the three methods detect failure nodes from mobile nodes disconnected from the WSN. If a failure detection method identifies a failed node, it increases the number of inside assets and decreases the number of outside assets. The nurse's reportsare used as a reference to confirm whether the number of failure nodes detected by the methods is correct.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Vertical Structure of Rainfall
#### 5.1.4 Remarks on the Vertical Structure of Rainfall  
Differences in the vertical structure of precipitation and its variation lay in rainfall types, sea-land difference and regional differences, and had a correlation with the surface rain rate. These differences can be understood by different thermodynamical conditions between sea and land, between stratiform rainfall and convective rainfall, and between different regions. The convective rainfall had prominent instability conditions and thus it could ascend to higher altitudes than the stratiform rainfall. There was more moisture over oceans than over land and thus the moist air would be condensed more easily at lower altitudes over oceans than over land.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Period** & **Location** & **Products (Version) of PR** \\ \hline [PERSON] et al. [114] & Dec. 1999 to Feb. 2000 & East and west Pacific & 2A25 (version5) \\ [PERSON] et al. [119] & Jan. 2000 to Oct. 2011 & southern California, Arizona, and & 2A23 and 2A25 (version7) \\ [PERSON] and [PERSON] [117] & Dec. 1997 to Aug. 2012 & Huaihe River Basin (HRB) of China & 2A23 and 2A25 (version7) \\ [PERSON] et al. [105] & 1998–2007 & Asia & 2A25 \\ [PERSON] et al. [106] & 1998–2007 & Asia & 2A25 \\ [PERSON] et al. [108] & Jun. to Sep. of 1998–2004 & West Africa & 2A25 (version6) \\ [PERSON] and [PERSON] [115] & 1998–2002 & Africa and Amazon & 2A25 \\ [PERSON] and [PERSON] [110] & 1998–2010 & West Africa & 2A25 \\ [PERSON] et al. [118] & Sep. 2001 to Aug. 2012 & tropics & 2A25 (version7) \\ [PERSON] et al. [113] & 1998–2007 & Southeast Asia & 2A25 (version6) \\ [PERSON] et al. [109] & 1998–2006 & tropics & the University of Utah \\ [PERSON] et al. [107] & 1998–2012 & India and adjoining oceans & 2A23 and 2A25 (version7) \\ [PERSON] and [PERSON] [116] & Jun. to Aug. 1998; & tropics & 3A25 (version4) \\  & Dec. 1997 to Feb. 1998 & tropics & 2A25 (version6) \\ [PERSON] and [PERSON] [112] & 1998–2010 & tropics & 2A25 (version6) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Key information of the studies on vertical structure of rainfall from PR data.
## 6 Sensitivity Analyses for the Retrieval of GOME-2A SiF Using PCs
### Conclusion of the Sensitivity Experiments  
Taking the results from the three experiments together, we can now better understand the comparison with our SiF values versus NASA (v26) set as discussed in the previous section. In the implementation of SiF NASA (v26), the O\({}_{2}\) A band is excluded, less PCs are used, and cloudy ocean spectra are selected to determine PCs for atmospheric transmission. Tested in isolation in the experiments discussed here, these three differences would all increase retrieved SiF values. However, the water vapour absorption band is also excluded in SiF NASA (v26) and this change would mean a decrease of retrieved SiF values as compared with our implementation. In the direct comparison of the SiF KNMI and SiF NASA (v26) data sets where all effects are combined, we see that SiF NASA values are smaller that SiF KNMI values (smaller slope in Figure 8). This suggests that the water vapour band has a strong impact on the retrieval. Further research may indicate whether this explains the low correlations over tropical rainforests.