esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Influence of Function Parameters  
Through sensitivity analysis, we found that the four parameters \(\mu_{1},\mu_{2},D,U\) have effects on the shape of the curves and the measurement results.  
1. When the service rate of non-congested \(\mu_{1}\) increases, \(C,k_{C}\) and \(\varphi_{d}\) all have a significant increase. The increase in the service rate in non-congested \(\mu_{1}\) means that the system has more capacity in the non-congested condition, so the system achieves a larger \(C,k_{C}\). A larger \(C\) could make a deeper sudden drop, which makes a larger \(\varphi_{d}\).
2. When the service rate of non-congested \(\mu_{2}\) increases, \(C\) and \(k_{C}\) fluctuate within a certain range, while there is a violent decrease in the value of \(\varphi_{d}\). The increase in the service rate in congested \(\mu_{2}\) means that the system has more capacity in the congested condition, so the system achieves a larger \(C,k_{C}\), and eases the sudden drop, which makes a lower \(\varphi_{d}\).
3. When the threshold at non-congested \(D\) increases, \(C\) and \(k_{C}\) have a slow increase, while there is a violent decrease in the value of \(\varphi_{d}\). The increase in the threshold at non-congested \(D\) means that the system will recover to be non-congested earlier, which eases the sudden drop and makes a lower \(\varphi_{d}\). However, \(D\) hardly affects the low-density area, so \(C,k_{C}\) increase very slowly.
4. When the threshold at congested \(U\) increases, \(C\) and \(k_{C}\) fluctuate within a certain range, while \(\varphi_{d}\) has a significant decrease. \(D\) hardly affects the low-density area, so \(C,k_{C}\) increase very slowly.  
The design significance of the above parameters is consistent with the results of its sensitivity analysis, which verifies the feasibility and validity of this model. In addition, due to the different magnitudes between the different fitting parameters, we did not compare their effects on each measurement result.
# Comparison of Fatigue Performances Based on Shape Change of Rail Fastening Spring
## 3 Comparison of Fatigue Performance Based on Fastening Spring Shapes
### Height of the Spring End  
The effect of height on fatigue performance was evaluated by changing the spring end height as a variable. The height here actually means the height difference between the free end and the support of the spring at the spring-to-rail interface. In Figure 5, the original shape of the fastening spring is shown in gray. The FE model, in which the height has been changed, is shown in orange. Three different heights of the fastening spring end (i.e., +10%, +15%, and +50% of the original height) were compared. For example, 10% in Figure 6 means the height increment in Figure 5 divided by the original height in Figure 5.  
Figure 5: Perspective view of change in the height of the fastening spring end.  
Figure 6 plots the global response of the fastening force vs. the displacement for various heights of the fastening spring end. Vertical displacement was applied to the torque location, and the displacement values corresponding to the rail fastening force (i.e., clamping force to the rail) of 9 kN were applied to the finite element models. Figure 7a shows the maximum principal stresses during fastening in Step 1, varying the height of the fastening spring, while Figure 7b shows the range of maximum principal stresses due to the vertical deflection caused by train use. Steps 1 and 2 yielded the stress range shown in Figure 7b. It can be observed from the results that the variation of the spring end height has little effect on the stress conditions of the fastening spring. This might be due to the fact that the internal forces (i.e., shear force and bending moment) on the critical sections of stress concentration have little change for each model because the moment arm from the loading location to the critical sections remained unchanged. In other words, the fatigue performance of the fastening spring may not be significantly affected by the change in the end height.
## 4 Discussion
### Limitations and Future Applications  
Varying responses of vegetation types and indices introduce uncertainties in daily water stress monitoring. The diurnal variations of vegetation structure and physiology in response to water stress differ among vegetation types such as grasslands, forests, and maize [12; 17; 23]. For instance, [PERSON] et al. reported that the AMR in cork oak was more sensitive to water stress due to partial stomatal reopening [23], which contrasts with our findings of a higher sensitivity of the NMR in the maize canopy to water stress. Such findings reflect different structural and physiological responses to drought between woody and non-woody plants. Therefore, it is crucial to conduct additional field experiments to validate the applicability of the NMR across various vegetation types. Our findings indicate that the sensitivity of the NMR to daily water stress differs significantly among these indices (Figures 15 and 16). Therefore, future research must focus on exploring additional indices to enhance the effectiveness of the NMR in monitoring daily water stress, for example, the Photochemical Reflectance Index (PRI) [48].  
This study demonstrated that the degree of drought on a given day can be accurately assessed by integrating the intraday variations in both the physiological and structural characteristics of vegetation. This finding has significant implications for the future advancement and application of drought remote sensing technologies. In the realm of drought detection via remote sensing, it is crucial to focus on the acquisition of intraday multi-temporal data. For small-scale areas, such data can be collected using unmanned aerial vehicle (UAV) remote sensing systems [51]. However, for larger areas or global scales, the development of geostationary satellites and satellite constellations may be necessary to enhance temporal resolution [52; 53]. In terms of practical applications, minimizing the interference from historical drought events and crop growth allows for a more precise evaluation of the actual water deficit on a given day. This, in turn, can aid in the guidance of precision irrigation strategies, promoting increased crop yield while optimizing the use of water resources.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results
### CWRU Dataset
#### 5.1.2 Vibration Load-Variant CWRU Dataset  
To validate the reliability, stability, and robustness, we investigated the fault identification capabilities of the SVM + FOR-ACO, the SVM + FOR-SPIO, and the SVM + FOR-PIO on the vibration load-variant datasets provided by CWRU when the cracks remain fixed. The average accuracy values (ACA) are shown in the following table (Table 9).  
Concerning this table, the average accuracy of the identification of the degradation for the SVM + FOR-ACO, which was used to validate the robustness, is about 97.2%. For the SVM + FOR-SPIO method, it is about 92.8%, and for the SVM + FOR-PIO plan, it is about 82.4%. Furthermore, the SVM + FOR-ACO outperforms the SVM + FOR-SPIO, yielding, on average, the accuracy of identification improved by about 5.8%, 3.8%, and 3.7% for 0.007, 0.014, and 0.021 inch crack sizes, respectively. Moreover, the SVM + FOR-ACO also outperforms the SVM + FOR-PIO, yielding, on average, the accuracy of identification improved by about 16.8%, 14%, and 13.5% for 0.007, 0.014, and 0.021 inch crack sizes, respectively. Regarding the power of the SVM + FOR-ACO for single-type fault detection and classification of a bearing for crack-variant and load-variant tests, the SVM + FOR-ACO is more persuasive than the other two schemes.
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 1 Introduction
### Paper Contributions and Structure  
The main contributions of the paper are as mentioned below:  
* Identification of a hardware infrastructure configuration that enables the automation of a hydroponic solution;
* Identification of possible software infrastructures compatible with the required hardware infrastructure;
* Analysis of the possibility of integrating an IoT infrastructure;
* Detailed comparative analysis between cloud infrastructure and local infrastructure for the two reference proposals of the research;
* The research analyzes the impact of using IoT equipment (MSStack, hydroponic monitoring sensors) in both cloud and local environments;
* Detailed presentation of a method for implementing the two infrastructures;
* Assessment of the impact of response time (RT), operational reliability (OR), implementation costs (IC), operational costs (OC), configuration scalability (CS), data accessibility (DA), and data security (DS) through the two infrastructures;
* The research provides applicable recommendations for farmers who wish to adopt modern and intelligent solutions to improve decision-making processes and achieve agricultural digitalization.  
The paper is structured into five sections. Section 2 presents the proposed hardware infrastructure, followed by the two software infrastructures analyzed, the automation scheme for liquid feeding of the hydroponic container, and the presentation of the seven indicators associated with the evaluation metrics of the two software infrastructures. Section 3 includes the results highlighting the comparative analysis of the two infrastructures. The discussion of the results, research limitations, and future works are depicted in Section 4. Finally, Section 5 describes the conclusions of the paper.
## 2 Hydrogen Value Chain
### End-Use on Hydrogen Value Chain
#### 2.1.1 Power-to-Gas Configuration  
Figure 5 shows a general arrangement of a typical P2G value chain, alluding to be mainly directed towards the decarbonization of the current natural gas system.  
This approach involves mixing (blending) hydrogen with natural gas, aiming for the gas networks to transport more energy from renewable sources than from fossil origin in the medium to long term [22]. Considering that both the technical characteristics of the end-use equipment (furnaces, turbines, boilers...) and those of the gas network itself impose limitations on the percentage composition of hydrogen in the mixture, there is also the option--or the need--to build dedicated hydrogen networks.
## 4 Cost Function to Be Minimized
### Other Cost Indexes  
Lastly, the remaining terms in Equation (14), namely \(C_{crystal}\ ^{\%}\), \(C_{vac}\), \(C_{cab}\), \(C_{man}\),\(C_{dis}\), and \(C_{other}\) are computed by multiplying the corresponding values per unit cable length, which are \(c_{crystal}\ ^{\%}\), \(c_{vac}\), \(c_{cab}\), \(c_{man}\),\(c_{dis}\), \(c_{comm}\), \(c_{install}\), and \(c_{other}\), respectively, by \(L_{line}\). It is worth noting that \(c_{crgu}\)\({}^{\%}\) is discounted with respect to the length of the cryostat to purchase, which is equal to \(L_{line}\).  
Regarding \(C_{ter}\), it is taken as a fixed value not requiring further multiplicative factors and which takes into account the cost of three-phase terminations at both sides of the cable.  
While \(C_{crysotal}\)\({}^{\%}\), \(C_{vac}\), \(C_{ter}\), \(C_{cab}\), \(C_{comm}\), \(C_{install}\), and \(C_{other}\) are capital costs, \(C_{man}\) and \(C_{dis}\) are operating costs which have to be discounted in a similar manner to that performed in Equation (25). In practice, this is carried out as follows:  
\[C_{man}=\sum_{z}^{yr}\ \frac{L_{line}\ c_{man}}{yr\ (1+\psi)^{2}} \tag{28}\]  
\[C_{dis}=\ \frac{L_{line}\ c_{dis}}{(1+\psi)^{yr}} \tag{29}\]  
where \(C_{dis}\) is discounted, supposing this cost component is paid directly at the end of the cable's life.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 4 Anomalies and Anomaly Detection in Blockchain
### Anomaly Detection in Blockchain Networks
Heuristic-based approaches: Drawing upon the wisdom of domain experts, heuristic-based approaches provide a valuable tool in the arsenal of anomaly detection [31]. These methods utilize expert knowledge and predefined indicators of suspicious activities to flag potential anomalies [23; 26]. By leveraging human expertise and intuition, heuristic-based approaches can rapidly identify behaviors that deviate from established norms or violate predefined rules [79]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Techniques** & **Strengths** & **Weaknesses** \\ \hline Statistical Analysis & Simple and interpretable approach; utilizes statistical measures to establish normal behavior patterns & Limited in detecting anomalies that deviate significantly from statistical measures \\ \hline Machine Learning & Ability to learn from historical data and adapt to evolving anomalies; detection of complex and subtle anomalies & Complexity and computational overhead in training and deploying models; may generate false positives or false negatives if the anomaly patterns change over time \\ \hline Network Analysis & Can capture systemic anomalies and identify network-level attacks & Limited visibility into encrypted transactions and activities; complexity in analyzing large-scale networks. \\ \hline Heuristic-based & Utilizes expert knowledge and predefined indicators of suspicious activities & Limited to detecting known patterns and predefined indicators; may struggle to adapt to new and evolving types of anomalies \\ \hline Deep Learning & Ability to apprehend non-linear data association; effective detection of anomalies that manifest convoluted patterns & Need for significant corpus of meticulously annotated training data, particularly when it comes to detecting anomalies, which are frequently infrequent occurrences; time-intensive training \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Anomaly detection frameworks and their characteristics. * Deep Learning: Deep learning models stand out for their remarkable ability to grasp intricate and non-linear associations within data [3]. These models excel at capturing complex patterns and fluctuations that may manifest convoluted relationships. By leveraging their non-linearity, deep learning models adeptly identify anomalies that may exhibit unusual patterns, previously unseen correlations, or subtle deviations from expected behavior [108; 109; 110].
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
###### Abstract  
Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.  
X-ray tomography technology; ResNet50; image processing; model reconstruction; mold 2024
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 2 Related Work
### Security Mechanisms for WBAN
#### 2.1.3 Channel Characteristic-Based Authentication Mechanisms  
Channel Characteristic based Authentication mechanisms are also known as location-based authentication systems; they are built based on variations in Received Signal Strength (RSS). Researchers have leveraged the variations in RSS over time to authenticate WBANs. Body Area Network Authentication (BANA) [9] is a lightweight authentication scheme built on the observation that RSS variations are distinct for on-body and off-body communication channels. An extended version of this approach is ASK-BAN [32], which works concurrently within a wireless channel to generate a key and node authentication. A static channel for authentication and a dynamic channel for key generation are employed. This system takes around 12 s for authentication and 15.9 s for key generation. ASK-BAN requires additional nodes between the Control Unit (CU) and the sensor node. In addition, sensor nodes on the body are deployed half a wavelength from each other to verify the viability of the multi-hop relay node security system. On the other hand, during the authentication phase of the system, the subjects cannot perform any bodily movement.
# Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink
## 4 Discussion
### Implications for the Management of Terrestrial Carbon Sinks  
According to the findings above, we can summarize that the spatial patterns, hotspot clusters, partial correlation coefficients, and driver types all show north-south differences, with Zhejiang as a dividing line. This may be because Zhejiang Province is an obvious turning area for coastal areas in terms of topography, land use types, and climate conditions. So, we recommend that policymaking of terrestrial carbon sink management in CCZ should take full account of its north-south differences and internal similarity, which can improve efficiency and reduce unnecessary costs. Our results show that despite the significant and widespread upward trend in NEP in areas north of Zhejiang Province, the magnitude of NEP there is much lower compared to southern regions. Therefore, northern areas should enhance the efforts and effectiveness of ecological conservation and improve vegetation planting structures, for instance, in the NCP. Southern regions are significant carbon sink contributors and are also areas with rapid economic development, and the relationship between NEP and HA could be more complicated there. Southern regions should proactively take measures to address the adverse impacts of human activities on NEP, which include enhancing the construction of green facilities within cities and implementing regional ecological conservation policies.  
Land use planning can exert a substantial influence on terrestrial ecosystems, potentially causing changes in the carbon balance [62]. Therefore, managing terrestrial carbon sinks from the perspective of land use has a practical and instructive significance. CCZ should control the urban land expansion and mitigate the encroachment on forests and farmland, which are dominant carbon sink contributors here. Although forests have enormous potential for carbon sequestration, and notable carbon benefits can be obtained from projects like afforestation and reforestation, carbon sequestration costs are significantly higher in the eastern coastal areas in comparison to other areas within China [63]. Therefore, the achievement of carbon neutrality in China's coastal areas should prioritize reducing carbon emissions caused by HA inside urban areas, maintaining the stability of forest systems, and enhancing the resilience of forests to extreme climates, especially in the provinces to the south of Zhejiang.
## 2 Overview of Machine Learning Techniques
### How Machine Learning Can Be Applied in Maritime Systems  
The use of Machine Learning (ML) in maritime systems presents an innovative approach to handle the complexity and dynamic nature of marine traffic management. Given the richness of data available, ML can significantly enhance the predictive and decision-making capabilities in these systems. Here are some specific ways in which ML can be applied:  
1. Vessel Traffic Prediction: Using historic AIS data and environmental conditions, ML algorithms can learn patterns in vessel movements and make accurate predictions. These predictions can include estimated times of arrival, potential congestions, and ideal routes for avoiding traffic [16].
2. Anomaly Detection: Unusual or anomalous vessel behaviors, such as unexpected route deviations or speed changes, can often indicate potential issues such as mechanical failures, piracy, or illegal activities. ML can be trained to recognize these anomalies in real time, allowing for early interventions and mitigations [31].
3. Risk Assessment: ML models can assess the risk of maritime accidents by analyzing a multitude of factors, such as weather conditions, vessel types, traffic density, and historical accident data. Such risk assessments can be used to inform safety measures, route planning, and other operational decisions [35].
4. Environmental Impact Analysis: The maritime industry significantly contributes to air and water pollution. ML algorithms can be trained on various data, like fuel consumption, engine type, and operational patterns, to predict emissions and other environmental impacts from vessels. These predictions can inform efforts to reduce the environmental footprint of maritime activities [36; 37].
5. Port Operations: Ports are critical nodes in maritime systems, and their efficiency greatly impacts the entire shipping industry. ML can be used to optimize various port operations, such as berthing schedules, loading and unloading operations, and resource allocation [31; 38].
6. Maritime Surveillance: Surveillance is crucial for maintaining maritime security. ML, combined with technologies like satellite imaging and radar data, can enhance maritime surveillance by identifying potential threats, tracking suspicious vessels, and monitoring protected marine areas [39].
7. Regulatory Compliance: ML can help ensure regulatory compliance by continuously monitoring vessel operations and conditions against established standards and regulations. Any deviations can be quickly identified and addressed [9].  
These applications underscore the transformative potential of Machine Learning in maritime systems. The ability to analyze vast datasets, predict future conditions, and automate complex decision-making processes can bring about significant improvements in the safety, efficiency, and sustainability of marine traffic management. The following sections will provide a more detailed examination of these applications and their impact on the maritime industry.