esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 5 Assessment of the Usefulness of the Collected Data
### Examination of Probability Distributions of Maintenance Tasks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distribution Type** & **Normal** & **Log-Normal** & **Weibull** & **Exponential** \\ \hline
**Distributor** & \(F(t)=\Phi\left(\frac{t-\mu}{\sigma}\right)\) & \(F(t)=\Phi\left(\frac{\mu t-\mu}{\sigma}\right)\) & \(F(t)=1-e^{-(\frac{t}{\sigma})^{\sigma}}\) & \(F(t)=1-e^{-\lambda(t-\gamma)}\) \\
**Distribution** & \(y_{i}=\Phi^{-1}[F(t_{i})]\) & \(y_{i}=\Phi^{-1}[F(t_{i})]\) & \(y_{i}=\ln(-\ln[1-F(t_{i})])\) & \(y_{i}=\ln[1-F(t_{i})]\) \\
**coordinates** & \(x_{i}=\mu,\ \mu(t_{i})\) & \(x_{i}=\ln(t_{i})\) & \(x_{i}=\ln(t_{i})\) & \(x_{i}=t_{i}\) \\
**Distribution** & \(\hat{\mu}=-\hat{a}\cdot\hat{\sigma}\) & \(\hat{\mu}=-\hat{a}\cdot\hat{\sigma}\) & \(\hat{\mu}=e^{-\frac{t}{\sigma}}\) & \(\hat{\mu}=-\frac{1}{\hat{b}}\) \\
**parameters** & \(\hat{\sigma}=\frac{1}{\hat{b}}\) & \(\hat{\sigma}=\frac{1}{\hat{b}}\) & \(\hat{\sigma}=\hat{b}\) & \(\hat{\sigma}=\frac{1}{\hat{b}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determined parameters of considered probability distributions. higher the value, the better the model fits the sample of data. Its values are most often expressed as a percentage. It can be calculated using the following formula:  
\[R^{2}=\left(\frac{\sum_{i=1}^{n}x_{i}y_{i}-\frac{\sum_{i=1}^{n}x_{i}\sum_{i=1}^{n }y_{i}}{n}}{\sqrt{\left(\sum_{i=1}^{n}x_{i}^{2}-\frac{\left(\sum_{i=1}^{n}x_{i} \right)^{2}}{n}\right)\left(\sum_{i=1}^{n}y_{i}^{2}-\frac{\left(\sum_{i=1}^{n} y_{i}\right)^{2}}{n}\right)}}\right)^{2} \tag{14}\]  
The probability distribution that best fits the sample of data is verified using the Kolmogorov-Smirnov match test. This test is used to verify the consistency of empirical distribution with the theoretical distribution of the continuous type [46]. An unquestionable advantage of the test is the possibility of using it when examining the distributions of very small populations of samples (\(n\)). The Kolmogorov test is based on a comparison of the empirical distribution function \(K(t_{i})\) (cumulants) with the theoretical distribution function \(F(t_{i})\), according to the following equation:  
\[D=\max_{1\leq i\leq n}\left|K(t_{i})-F(t_{i})\right| \tag{15}\]  
At the significance level \(\alpha\) = 0.05, we will conduct a statistical test in which  
**H0**: _The distribution represents the data as follows_:  
\[H0:\ K(t_{i})=F(t_{i})\Longleftrightarrow D<D_{crit} \tag{16}\]
**H1**: _The distribution does not represent the data as follows_:  
\[H1:\ K(t_{i})\
eq F(t_{i})\Longleftrightarrow D\geq D_{crit} \tag{17}\]  
From the tables for the Kolmogorov distribution, the critical value \(D_{crit}\) for the given significance level \(\alpha\) and the size of the tested sample \(n\) could be read. If \(D<D_{crit}\), then at the significance level \(\alpha\), there are no grounds to reject the null hypothesis \(H0\), while if \(D\geq D_{crit}\), then we reject the null hypothesis \(H0\) in favor of the alternative hypothesis \(H1\).
###### Abstract  
As industries develop and information accelerates, enterprise collaboration is increasing. However, current architectures face malicious attacks, data tampering, privacy issues, and security and efficiency problems in information exchange and enterprise credibility. Additionally, the complexity of cyber threats requires integrating intelligent security measures to proactively defend against sophisticated attacks. To address these challenges, this paper introduces an intelligent and secure cloud-edge collaborative industrial information encryption strategy based on credibility assessment. The proposed strategy incorporates adaptive encryption specifically designed for cloud-edge and edge-edge architectures and utilizes attribute encryption to control access to user-downloaded data, ensuring secure information exchange. A mechanism for assessing enterprise credibility over a defined period helps maintain a trusted collaborative environment, crucial for identifying and mitigating risks from potentially malicious or unreliable entities. Furthermore, integrating intelligent threat detection and response systems enhances overall security by continuously monitoring and analyzing network traffic for anomalies. Experimental analysis evaluates the security of communication paths and examines how enterprise integrity influences collaboration outcomes. Simulation results show that this approach enhances enterprise integrity, reduces losses caused by harmful actors, and promotes efficient collaboration without compromising security. This intelligent and secure strategy not only safeguards sensitive data but also ensures the resilience and trustworthiness of the collaborative network.  
cloud-edge collaboration; information security; attribute encryption; credibility assessment
## 5 Discussion  
With the rapid development of GNSS, more researchers have been investigating its applications in meteorology. Most of these previous studies only involve both post-processing and near-RT modes [4; 5; 6]. However, sufficient information of the atmosphere state should be delivered with short or even no latency for some innovative applications, such as nowcasting and short-term weather forecasting. Different from previous studies, we focus on the actual real-time retrieving of tropospheric delays in this paper. To achieve this, the real-time satellite clock and orbit products and multi-GNSS data streams are required. In Section 4.1, we assessed the quality of the real-time products derived from various operating IGS services. As shown in Figures 3 and 5, the real-time orbit and clock accuracies for emerging BDS and Galileo satellites are still not comparable to those for GPS and GLONASS satellites. Moreover, the lower accuracy of Galielo and BDS ephemerides doses not directly translate to ZTD results, what is seen in Table 5 where the results for G/R and G/R/E/C are very similar. The qualities of real-time products for BDS and Galileo are expected to be further improved with the setup of more GNSS tracking ground stations in the future.  
Based on real-time orbit and clock products and data streams, the real-time tropospheric parameters can be estimated by using the real-time PPP technology. In Section 4.2, we evaluate the accuracy of real-time ZTDs comparing with the final tropospheric products for the different solutions. The results confirm that the performance of real-time retrieving tropospheric delays can be improved due to the fusion of multi-GNSS compared to the single-system constellation. In addition, we also demonstrate that the fixing coordinate mode is more suitable for extracting real-time ZTD estimates based on real-time products derived from different IGS-RT services. In the multi-GNSS solution, the ZTD accuracy in the fixing coordinate mode can be improved by a factor of approximately 2.7 in comparison to that in the kinematic mode.  
With the launch of more new satellites and setup of more ground GNSS stations, the quality of real-time orbit and clock products provided by IGS-RT services will be improved, especially for BDS and Galileo. Consequently, the real-time retrieved ZTDs with higher accuracy can be achieved by using multi-GNSS real-time PPP technology.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.1.2 Local Spectrum Awareness  
For the local spectrum scenario, nodes must build the spectrum occupancy database based on their local sensing activity. For sensor nodes, it will be impossible for each sensor to sense through the whole spectrum for opportunities; thus, adequate sensing schemes that are suitable for sensor nodes and can meet service demands must be implemented. Ref [54] advocates the need for spectrum sensing algorithms that utilize a minimum number of samples to detect PUs within a specific detection error probability to improve energy conservation/minimization of transmitting nodes.  
In this respect, cooperative sensing schemes are more favored over individual sensing because distributing the duty of spectrum sensing among the nodes can drastically reduce the energy demand on the nodes and increase the time for data transfer. In this light, [55] advocates that cooperative sensing is more favorable for request to send (rts) effectiveness in improving SU detection accuracy, but the issues of information fusion and distributed spectrum sensing are still open.  
Ref [56] proposes a method of cooperative spectrum management. This two-layered hierarchical model uses a centralized sensing scheme. However, their proposal does not practically address the main issues of CR such as DSA, spectrum handoff, _etc._ Other credible concepts that can be utilized or incorporated while designing distributed sensing algorithms have been presented in [57], which minimizes the energy used in distributed sensing by obtaining a Negmon-Pearson and Bayesian formulation to optimally choose the sleeping and censoring design parameters, and [58], which develops distributed spectrum sensing and channel selection for WPANs. The learning engine derives its cognition from the exchange of SOP. Various learning schemes have been implemented in this regard to help the cognitive engine gradually build up a knowledge base of spectrum opportunities so that it can quickly adapt to its environment to make better, more informed routing decisions. This method is similar to that presented in [59], which is a DSA scheme with learning for CR conducted by awarding weights based on the learning experience.
## 4 Discussion
### Factors Affecting the Dynamics of LUEmax  
Previous studies have shown that dynamic LUEmax values allow the model to better fit the adaptive response of leaves to short-term environmental changes, thus facilitating a more accurate calculation of gross primary productivity [81]. However, the LUEmax parameter is closely related to the physiological state of vegetation. In addition to factors such as temperature, LAI, and moisture, the nutrient element limitation, tree age, and proportion of diffuse radiation also affect it. The difference in LUEmax values may be caused by the seasonal variation characteristics of the original study area being limited by its single local climate. On the other hand, it may also result from the differences in community species within the same vegetation type. Studies have pointed out that the seasonal variation in the LUEmax parameter is closely related to the LAI and canopy structure [82], and the change in its value can be explained by ambient temperature [83; 84]. Many studies have examined the interannual variations and seasonal dynamics of LUEmax, revealing that its fluctuations are influenced by environmental factors, such as radiation variability, vapor pressure deficit (VPD), and temperature [85; 34].  
Consistent with the findings in this study, previous research has demonstrated that TA, TS, NETRAD, VPD, LSWI, and EVI are important drivers of LUEmax. At the same time, the extent of their relative importance differs depending on the specific vegetation type and environmental context. These studies also found that different plant species exhibit significant differences in LUEmax. Forest ecosystems, such as ENF and DBF, depend strongly on radiation and TS conditions, while GRA and croplands (CORN) are more sensitive to the EVI and TA. WET show a reliance on the EVI and LAI. The interplay between environmental (e.g., temperature, radiation, VPD, and CO2_F) and biophysical factors (e.g., LAI and EVI) drives the dynamic variation in LUEmax across different vegetation types. These findings demonstrate that the drivers of LUEmax are highly vegetation-specific, shaped by the interactions between environmental conditions and ecosystem functional traits. Among these factors, except the CFlux light-use efficiency model [86], which considers the tree age factor, most single-leaf light-use efficiency models do not have corresponding model parameter designs for the remaining factors. Therefore, adding model parameters for factors affecting LUEmax in the existing model structure and simultaneously calibrating all model parameters uniformly will help improve the accuracy of GPP estimations by the light-use efficiency model.
## 3 Results and Discussion
### Nutritional Screening and Morphofunctional Assessment
#### 3.2.4 Is It Necessary to Screen for Sarcoma in These Patients?  
**Recommendation 8**.: _GARIN suggests screening for sarcopenia in all patients diagnosed with COPD._  
Grade of recommendation GPP, (98%) Strong consensus.  
Comment:  
It has been shown that patients with low lung function, even healthy patients without COPD, are associated with decreased muscle mass [73].  
The coexistence of COPD/asthma and sarcopenia in the elderly is common, and studies show a prevalence of around 15-25% [11; 74]. These associations have been shown to further increase quality of life impairment and reduce the physical capacity for exercise [74].  
Further, patients with pulmonary pathology and a higher degree of sarcopenia are associated with a higher percentage of osteoporosis, especially asthma-COPD overlap syndrome [75].  
Patients with a low exercise capacity (6 MWD < 350 m) and those with a decrease in physical activity (<7128 steps/day) are associated with an increase in exacerbations, a poorer quality of life, higher scores on depression scales, and decreased anthropometric and laboratory parameters related to disease prognosis and mortality [57].  
Finally, a statistically significant relationship between the presence of sarcopenia and non-alcoholic fatty liver disease (NAFLD) (highly prevalent in COPD patients) has been determined after multivariate analysis, excluding other risk factors [76], as well as between sarcopenia and the presence of metabolic syndrome, mainly in men with a restrictive pulmonary pattern [77].  
The members of GARIN, therefore, propose that, although no literature references have been found in the search, the use of new tools, such as the SARC-F, could be very useful in screening these patients for sarcopenia.
# In-the-Loop Simulation Experiment of Aero-Engine Fault-Tolerant Control Technology
## 3 Results  
### Fault Tolerant Control  
#### 3.1.1 Fault-Tolerant Control Based on Analytical Redundancy  
The schematic diagram of fault-tolerant control based on analytical redundancy is shown in Figure 2; taking the high-voltage speed sensor as an example, if it does not fail, the sensor value of the real engine is used for PID control. If it fails, the analytical redundancy, that is, the estimated value corresponding to the adaptive model, is used to replace the error value of the high-voltage speed sensor as the feedback signal for closed-loop control, so as to isolate the failed sensor and ensure the stability of the system.  
Due to the limitation of space, the fault of the Nh sensor is taken as an example. Figure 3 shows the schematic diagram of the change of the three correlation values of the high and low speed when the sensor fails by adopting residual zero and analytic redundancy fault-tolerant control. The sensor fault is simulated by injecting 5% bias fault.  
The simulation process is as follows: at the design point, the closed-loop control is switched at 1 s, the filtering is turned on at 5 s, the injection performance degrades at 10 s, and the sensor fault of high voltage rotation speed is injected at 25 s. The PLA increases uniformly from 40 s to 44 s, decreases uniformly from 44 s to 50 s, increases uniformly from 50 s to 54 s, and stops running at 65 s.  
Figure 3a,b simulation experiments show that after the failure of the Nh sensor, when the value of the fault sensor is used as the feedback quantity of the closed-loop control, the real operating parameters of the engine are all higher than the normal working state, and accidents are easy to occur. However, if other sensors fail, although the value of the corresponding fault sensor is reduced, because of taking the method of changing the residual to zero that [PERSON] filter needs, when the sensor fails, the fault sensors are quarantined and the real operation parameters of the engine are still in the normal working state, i.e., the fault-tolerant effect is better.  
Figure 2: Schematic diagram of fault tolerant control based on analytical redundancy.  
Figure 3c,d simulation results show that the Nh fault sensor is successfully excluded from the control system because the analytic redundancy of the Nh fault sensor, namely the Nh estimated value of the airborne adaptive model, is used as the feedback quantity after the failure of the Nh sensor, thus ensuring stable and reliable operation. In addition, the effect of performance degradation at 10 s can be seen in Figure 3c,d, but the performance degradation at 10 s cannot be seen obviously because the range of Figure 3a,b ordinate is large. The black line indicates the normal output, that is, the parameter value when the engine performance degradation does not occur. Therefore, when a fault occurs and fault-tolerant control is adopted, the three lines will no longer coincide, which can also reflect the effect of fault-tolerant control.
## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems
### Safety, Compliance, and Sustainability  
Safety is an uncompromising imperative in the maritime domain. The physical scale of autonomous vessels, the potential environmental consequences of failure, and the lack of immediate human oversight mean that AI agents must be designed with reliability and resilience at their core. Unlike conventional AI systems where failures may lead to operational inefficiencies or financial loss, failures at sea can result in collisions, oil spills, cargo damage, or even loss of life [75; 86; 106; 107].  
To address this, maritime AI agents integrate multiple fail-safe and redundancy layers. These include hardware redundancy (e.g., duplicate sensor arrays), software-level recovery protocols, and real-time fault detection algorithms. Agents are trained and tested across diverse operational scenarios, often using digital twins or simulation environments to assess how they perform under rare but critical edge cases [108].  
In addition to technical safety, compliance with maritime regulations is a defining characteristic that distinguishes maritime AI agents from general AI systems. Agents must actively uphold international conventions such as the following:  
* SOLAS (Safety of Life at Sea) [63];
* MARPOL (Prevention of Pollution from Ships) [64];
* CII (Carbon Intensity Indicator) [65];
* EU ITS framework for traffic communication [66].  
This requires embedded mechanisms to monitor emissions, verify adherence to route corridors, control ballast water discharge, and log operations in legally acceptable formats. The AI agent becomes, in effect, a regulatory actor, ensuring that autonomous decisions remain both operationally efficient and legally compliant.  
A growing focus on environmental sustainability further elevates the role of maritime AI agents. Beyond simply adhering to emissions standards, agents must actively optimize energy consumption, reduce carbon footprints, and balance propulsion strategies with environmental goals. They may, for instance, reroute ships to harness favorable currents, reduce speed to cut fuel consumption, or delay port entries to avoid congestion, all while meeting delivery schedules and ensuring safety [89; 90; 91].  
These layers of responsibility--safety, compliance, and environmental stewardship--are rarely embedded so centrally in other AI domains. Conventional systems may address some of these aspects, but typically as peripheral concerns. For maritime AI, they are core functional requirements.  
Thus, maritime AI agents operate not only as technical systems but as legally and ethically aware actors, accountable for safe and sustainable behavior in real time.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 4 Performance Evaluation
### The Packet Loss Rate  
To verify the performance of the proposed CARA algorithm in alleviating network congestion, we simulated the network packet loss rate, which varies with the length of the node cache, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of nodes & 100–300 \\ Maximum transmission range & 30 m \\ Data packet size & 1024 bits \\ Cache queue length & 20–50 packets \\ Sink coordinate & (50,50) \\ Data rate & 4096 bit/round \\ Simulation time & 400 round \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameter settings.  
Figure 3: \(Cont\).  
In Figure 3, the numbers of nodes in the three subgraphs are 100, 200, and 300. As the number of network nodes increases, the packet loss rate increases due to the increase in forwarding tasks that the nodes must undertake. However, the packet loss rate of the CARA algorithm at different cache queue lengths and node numbers is lower than the other two algorithms. This is because both the CCOR algorithm and the TER algorithm only make routing selections based on the traffic load of the node itself. The CARA algorithm takes into account both the cache remaining rate and the forward average cache remaining rate. These two parameters can accurately reflect the congestion of nodes and forward regions. Especially in respect of the number of nodes, the ability of our algorithm to avoid congestion nodes and areas becomes more evident.  
Therefore, when the number of nodes increases, the comparison algorithms' packet loss rate has a more noticeable increase, but the CARA algorithm's packet loss rate increases slightly. Thus, the CARA algorithm can more effectively balance the network load, and the overall packet loss rate performance is better than the other two algorithms. Furthermore, for the same number of nodes, the difference between the three algorithms gradually decreases with cache queue length. Thus, as the cache queue length increases, the routing algorithms alleviate the node's congestion to a certain extent while the data generation  
Figure 3: The packet loss rate varies with the size of the node cache. (**a**) The number of nodes = 100; (**b**) the number of nodes = 200; (**c**) the number of nodes = 300.