esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Response of Eggplant Growth to Foliar and Fertigation of AGE and ASA  
#### 3.2.1 Influence of AGE and ASA Applied as Foliar and Fertigation on the Morphology of Eggplant  
The morphological data of the eggplant seedlings is given in Table 2. Analysis of variance showed that plant growth was influenced significantly by AGE and ASA, and the effect in some cases seemed dependent on the method of application rather than the concentration in question. AGE foliar application increased plant height (4.7 cm), followed by fertigation (4.4 cm). The stem diameter, however, revealed no significant differences among these treatments, but compared to control plants, AGE application significantly altered the stem diameter. In root length, a significant influence of AGE application was observed on the treated eggplant (Figure 5).  
Other parameters such as shoot fresh weight, root fresh weight and soluble sugar contents indicated significant effects of AGE and ASA compared to the control treatment. Overall, findings of the study revealed that AGE foliar application has significant influence on the growth and development of eggplant, increasing its height, root length, shoot/root fresh, and dry weight.  
Figure 4: Influence of AGE and ASA on the antioxidative response and Lipid peroxidation levels of pepper seedlings. Bars represent means and standard errors of triplicates. Means followed by same letters are not significantly different from each other. (GS) AGE foliar, (GR) AGE fertigation, (SS) ASA foliar, (SR) ASA fertigation, (CK) distilled water foliar, and (CKR) distilled water fertigation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Method of Application**} & \multirow{2}{*}{**Plant Height**} & \multirow{2}{*}{**Stem**} & \multirow{2}{*}{**No. of Leaves**} & \multirow{2}{*}{**Root Length**} & \multirow{2}{*}{**Shoot Fresh**} & \multirow{2}{*}{**Root Fresh**} & \multirow{2}{*}{**Root Fresh**} & \multirow{2}{*}{**Shoot Dry**} & \multirow{2}{*}{**Root Dy**} & \multirow{2}{*}{**Root Stoot**} & \multirow{2}{*}{**Solable Sugar**} \\  & & & **(mm)** & & & **(mm)** & & & **Weight (g)** & & **Weight (g)** & & **Weight (g)** \\ \hline \multirow{2}{*}{AGE} & Polar & \(4.7  
3.2.2. The Chlorophylll and Carotenoid Contents of the Eggplant Applied with AGE and ASA  
Figure 6 represents the chlorophyll and carotenoid contents of eggplant treated with foliar spray and fertigation of AGE and ASA. Analysis of variance revealed that both chlorophyll a and b were not significantly different in the observed plants; however, the total chlorophyll content of the plants was significantly increased due to application of AGE and ASA treatment compared to that of control plants. On the other hand, there was a significant increase in the carotenoid content of the plants treated with AGE foliar and fertigation compared to the other treatments.  
Figure 5: Effect of AGE and ASA in the development of root of eggplant seedlings.  
Figure 6: _Cont._
### Contributions  
This study makes the following seven distinct contributions to the field of privacy-preserving, triage-oriented CDSS:  
* **End-to-end, triage-focused architecture.** Designed a modular framework that fuses structured (vitals, labs) and unstructured (clinical notes) data, integrates LLMs, FL, big-data infrastructure, and digital-twin concepts, and achieves near-real-time latency under typical inference conditions.
* **Real-world infectious disease triage case study.** Applied the CDSS to a de-identified infectious disease ED cohort (n \(\approx\) 132), achieving 75.8% first-pass agreement with expert reviewers on diagnosis, justification, and next-step recommendations.
* **Large-scale language-model benchmarking.** Fine-tuned a Llama 3.1-708 model that achieves up to 74% overall accuracy (83% on USMLE Step 2) across 750 curated USMLE-style questions--substantially outperforming baseline LLMs and medical-student means.
* **Privacy-preserving model-development pipeline.** Coupled secure FL (differential privacy and secure aggregation) with SGX-isolated inference, enabling multi-institutional collaboration while remaining fully GDPR/HIPAA compliant.
* **FAIRification workflow and ontology-mapping service.** Implemented a reproducible pipeline that aligns all ingested resources, assigns persistent identifiers, enriches metadata, and harmonizes codes via an Ontology-Mapping and Terminology Service (OMTS)--ensuring data are Findable, Accessible, Interoperable, and Reusable.
* **Event-driven, low-latency data-fusion engine.** Demonstrated horizontal scalability under doubled ED loads using Apache Kafka, Spark, and Node-RED to orchestrate streaming vitals, IoT signals, and EHR updates without performance loss.
* **Four-pillar GDPR compliance framework.** Mapped key articles of Regulation (EU) 2016/679 to concrete technical and organizational controls--including automatic DPIA generation, rights-of-erasure workflows, and continuous privacy KPIs--providing verifiable legal conformity.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
###### Abstract  
The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.  
Keywords:ship; interacting multiple model; extended Kalman filter; GPS; inertial navigation system +
Footnote †: journal: Applied Sciences
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 3 Results  
### TEC Accuracy  
Figure 3 shows the monthly mean VTEC error for the high/middle/low latitudes and for the NeQuick G and Klobuchar model for the NMA and TUW network of stations as described above. Annual variations of the errors can be seen for the high and mid-latitudes. These variations are of opposite signs for the two models, i.e., when the Klobuchar-derived error decreases, the NeQuick G-derived error increases, and vice versa.  
At high latitudes, the NeQuick G errors vary around zero, approximately between \(-2\) and \(+2\) TECU. The Klobuchar errors have an offset from zero of about 5-6 TECU, and slightly higher peak-to-peak values during a year. There may be a long-term decreasing trend in the Klobuchar error values. In the last quarter of 2021, the largest discrepancy (values comprises between 10 TECU and 15 TECU) in the time series error of the two models over the analysed period is observed. This may be related to the increasing ionization and space weather activity as the solar activity increases in the current solar cycle 25. At mid-latitudes, NeQuick G and Klobuchar have similar error magnitudes but with opposite offsets from zero. On average NeQuick G is slightly closer to zero, but this difference between Klobuchar and NeQuick G errors is smaller than the offsets of their individual time series and the inherent variations in the time series. At low latitudes, there is no obvious annual trend. Instead, both error graphs appear to have a long-term trend. This time series is not long enough to determine if this is related to the 11-year solar cycle, but this would be the most plausible explanation. It is known that the general ionization level varies with the solar cycle, and as the low latitudes have the largest TEC levels, they will also have the largest magnitude of long-term variation.  
Figure 2: Monthly means of the daily **(a)** HPE 95% and **(b)** VPE 95% for the Nequick G and Klobuchar models. The top panels in **(a,b)** show results for high latitudes, the middle panels for middle latitudes divided into northern and southern hemispheres, and the bottom panel for low latitudes. The left panels present the GRC, the middle panels the SRC, and the right panels the NMA/TUW results. The text line at the top of each subplot includes information about the number of used stations (e.g., #5) and the average together with standard deviation over the complete period for the NeQuick G solution (e.g., N: \(2.4\pm 0.5\) m) and Klobuchar solution (e.g., K: \(4.2\pm 0.5\) m).
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 5 Clustering and Tracking
The Kalman filter computes an _a posteriori_ state estimate \(\hat{x}_{k}^{\prime}\) as a linear combination of an _a priori_ estimate \(\hat{x}_{k}\) (predicted state) and a weighted difference between the sensor originated measurement \(z_{k}\) and a measurement prediction \(H_{k}\hat{x}_{k}\). The posteriori state estimate or the corrected state \(\hat{x}_{k}^{\prime}\) of the target is written as:  
\[\hat{x}_{k}^{\prime}=\hat{x}_{k}+K_{k}(z_{k}-H_{k}\hat{x}_{k}) \tag{31}\]  
where \(K_{k}\), \(z_{k}\)\(H_{k}\) are the Kalman gain, the measurement vector (=cluster centroids) and the observation matrix, respectively. The corrected state \(\hat{x}_{k}^{\prime}\) gives the refined coordinates of the moving ship in range-Doppler domain. An exemplary tracking result was already shown in Figure 1d. Figure 20: **(a)** Range-Doppler image showing the total range of the complete scene and 128 Doppler bins respectively (cf. black box in Figure 4 bottom right). The ship signal is highlighted by the red box. **(b)** Scaled detail of the red box from **(a)**; **(c)** the clustered ship signal with its centroid and the bounding box. The ship pixels shown in this example were detected using the K-distribution.
# Maritime Supply Chain Optimization by Using Fuzzy Goal Programming
## 3 Proposed Model
### Definition of Membership Functions  
In this study, linear membership functions are preferred based on fuzzy goal and fuzzy constraints solution methodology. General structure of membership function is illustrated as follows:  
\[\mu_{h}(x)=\begin{cases}Y_{i}(x)<g_{i}&\Rightarrow 1\\ g_{i}<Y_{i}(x)<g_{i}+h_{i}&\Rightarrow\frac{(g_{i}+h_{i})-Y_{i}(x)}{h_{i}}\\ Y_{i}(x)>g_{i}+h_{i}&\Rightarrow 0\end{cases}\]where \(Y_{i}(x)\) is an objective function, and \(g_{i}\) and \(h_{i}\) are the lower bound and the tolerance level, respectively (Figure 2).  
In the structured model, triangular fuzzy numbers are used that represent the vague demand quantities. In the Figure 3, triangular fuzzy demand is shown as \(\tilde{M}_{dt}=(\tilde{M}_{dt}^{p},\tilde{M}_{dt}^{m},\tilde{M}_{dt}^{o})\), where \(\tilde{M}_{dt}^{p},\tilde{M}_{dt}^{m}\) and \(\tilde{M}_{dt}^{o}\) show the possible pessimistic, probable and optimistic values of demand.  
Weighted average method is preferred for the defuzzification of fuzzy demand. According to lolarable minimum membership function \(\alpha\) (alpha), fuzzy constraint is designed as non-fuzzy constraints as shown in Equations (10) and (11). ([PERSON], 1992; [PERSON], [PERSON], 2008, 2009; [PERSON], 2006, 2008, 2011).  
\[R_{d,t-1}+L_{dt}-R_{dt}\geq w_{1}\cdot\tilde{M}_{dt,\alpha}^{p}+w_{2}\cdot \tilde{M}_{dt,\alpha}^{m}+w_{3}\cdot\tilde{M}_{dt,\alpha}^{o} \tag{9}\]  
\[L_{dt}\geq w_{1}\cdot\tilde{M}_{dt,\alpha}^{p}+w_{2}\cdot\tilde{M}_{dt,\alpha} ^{m}+w_{3}\cdot\tilde{M}_{dt,\alpha}^{o} \tag{10}\]  
Here, \(w_{1}\), \(w_{2}\), \(w_{3}\) represent the pessimistic, probable and optimistic values that shows the decision maker's priorities.  
\[w_{1}+w_{2}+w_{3}=1\text{ where }0\leq w_{1},w_{2},w_{3}\leq 1 \tag{11}\]  
Figure 3: Fuzzy triangular demand.  
Figure 2: Linear membership function for \(i\)th goal.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 2 Experiment Design  
### Vehicles and Test Routes  
To analyze the vibration level for Korea's truck transport environment, we selected three types of trucks, as shown in Figure 2, which are commonly used to transport palletized unit-loads in Korea: a leaf-spring (suspension) 5-ton (payload capacity) truck, an air-ride 5-ton truck, and a leaf-spring 11-ton truck [2]. In this study, the trucks were unloaded. All of the selected trucks were built in Korea; their detailed specifications are shown in Table 1.  
The test route configured for measuring the vibration levels of the target trucks is shown in Table 2. The route consisted of a mixture of 110.6 km of highway and 191.3 km of national roads considering the characteristics of Korea's freight transport. The highway pavement was also divided into two types: 60.9 km of concrete-surfaced highway and 49.7 km of asphalt-surfaced highway. Moreover, the travel speed of the truck during the test was 90-100 km/h on highways and 60-90 km/h on national roads.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Leaf-Spring 5-Ton Truck** & **Air-Ride 5-Ton Truck** & **Leaf-Spring 11-Ton Truck** \\ \hline Brand & DAEWOO NOVUS 2007, Korea & DAEWOO PRIMA 2016, Korea & DAEWOO NOVUS 2005, Korea \\ No. of axles & 3 & 3 & 3 \\ Load capacity & 5 ton & 5 ton & 11 ton \\ Suspension & leaf-spring & air-ride & leaf-spring \\ Load platform & L \(\times\) W = 7500 \(\times\) 2350 mm & L \(\times\) W = 8500 \(\times\) 2400 mm & L \(\times\) W = 10,000 \(\times\) 2400 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of trucks used in the vibration level measurement.  
Figure 2: Various trucks used in the vibration measurement study (**a**) Leaf-spring 5-ton truck (**b**) Air-ride 5-ton truck (**c**) Leaf-spring 11-ton truck.
## 6 Numerical Results and Discussion
### Algorithm Analysis  
Figure 4 shows the performance of the two DRL-based caching algorithms proposed. One is based on the traditional deep Q-network IoV caching algorithm (DVQC), and another is the P3 DVQC algorithm which integrates the prioritized experience replay, the dueling, and the double deep Q-network technologies. The size of the experience replay pool is 3000 in the simulation. Figure 4 also shows that the P3 DVQC completes the convergence at nearly 5000-time slots, while the DVQC at about 6000-time slots. That is due to the P3 DVQC employing the PER technology improving the deep neural network training efficiency. In addition, the P3 DVQC shows more stability than DVQC at the convergence state, this is because the P3 DVQC integrated DDQN and Dueling technology, over-estimation of DQN is effectively eliminated, avoiding unnecessary misselections to achieve rapid convergence to the optimal caching strategy.  
Figure 5 shows the convergence performance comparison between the P3 DVQC and the DVQC. Before 3000 time slots is the experience replay pool initialization stage. After 3000 time slots, the P3 DVQC achieves a faster convergence rate than DVQC. That is due to the P3 DVQC employing the PER to achieve TD weighting of the experience pool samples, which makes deep neural networks more effective. The P3 DVQC algorithm determines the priority of the sample selected for training according to the size of TD errors to improve the training effect and speed up the convergence speed. However, the DVQC algorithm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Definition** & **Parameter** & **Value** \\ \hline \(P_{bs}\) & 40 dbm & \(\varphi_{MEC}\) & \([500,2000]\) Mb \\ \(\gamma\) & \([0.1,0.9]\) & \(\beta\) & \([0.5,0.8]\) \\ \(\mu_{m}\) & \([0.4,1.5]\) & \(\alpha\) & \((0,1]\) \\ \(\epsilon\) & 4 & \(\delta^{2}\) & \(-102\) dBm \\ \(P_{r}\) & 33 dbm & \(L\) & \([5,10]\) \\ \(\theta\) & \(\pi/6\) & \(C_{MBS}\) & \([3,6]\) Mbps \\ \(\mathcal{F}\) & \([10,50]\) & \(C_{BM}\) & \([10,100]\) Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: System Parameters.  
selects samples for training through a uniform sampling strategy, so the the convergence speed is slower than P3 DVQC.
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 3 Results
### Analysis and Observations
#### 3.4.2 Multiple Management Consoles  
Although several management frameworks are available for Apache Hadoop, such as Apache Ambari and the Cloudera Manager, a single management console that fully integrates across the ecosystem is not available. Interfaces for system security management, such as the Apache Ranger Admin WebUI, are separate from other system management consoles, such as Apache Ambari.  
A limitation of the Apache Ambari and Cloudera Manager is that they must be used to set up the cluster that they manage. One cannot add these managers to clusters that are already set up. These tools are designed to tightly couple with their Hadoop configuration.  
From a security perspective, these management tools help configure or \"turn on\" security components but do not confirm whether the system is fully secure. Validating the enforcement of the security policy requires interfacing to separate consoles, including directory systems and the Ranger Admin WebUI. Confirming that the system is correctly configured and reviewing its security status requires examining multiple interfaces and files. Automating the collection of this status-related information requires significant development of custom scripts that push logs to a central location.
## 2 Materials and Methods
### HPLC Analysis of C3G  
C3G content was analyzed as described by [PERSON] et al. and [PERSON] et al. [49; 50], with slight modifications. HPLC-PDA analysis was performed using the Dionex Ultimate 3000 HPLC system (Thermo Fisher Scientific, Waltham, MA, USA) coupled with the Dionex Ultimate 3000 photodiode array (PDA-3000; Thermo Fisher Scientific, Waltham, MA, USA). Chromatography was performed on the Kromasil C18 column (4.6 mm \(\times\) 250 mm, 5.0 \(\mu\)m; Tedia, Rio de Janeiro, Brazil), and the column temperature was maintained at 30 \({}^{\circ}\)C. The mobile phases consisted of 0.1% (\(v\)/\(v\)) formic acid in distilled water (A) and 0.1% (\(v\)/\(v\)) formic acid in acetonitrile (B). The parameters of HPLC analysis were as follows: 0-20 min: 90% A, 10% B; 20-21 min: 80% A, 20% B; 21-27 min: 20% A, 80% B; 27-28 min: 20% A, 80% B; 28-35 min: 90% A, 10% B gradient system. The analysis time was 35 min, the detection wavelength was 516 nm, the sample injection volume was 10 \(\mu\)L, and the flow rate was 1.0 mL. HR2302-30E sample (100 mg) was mixed with 10 mL of 0.1% (\(v\)/\(v\)) HCl-methanol and extracted via sonication (POWER SONIC 505; Hwashintech, Daegu, Republic of Korea) for 60 min. The solution was filtered through a 0.45-\(\mu\)m PTFE syringe filter and used as the test solution. A stock solution was also prepared by mixing C3G (200 mg) with 10 mL of 0.1% (\(v\)/\(v\)) HCl-methanol. The stock solution was diluted with 0.1% (\(v\)/\(v\)) HCl-methanol to prepare standard solutions with concentrations of 12.5-200 \(\mu\)g/mL.
## 4 Security and Privacy in Edge Frameworks  
Edge processing has evolved in response to the rapid growth of connected devices that continuously generate large volumes of data. In time-critical applications such as autonomous vehicles, healthcare, and traffic management, where latency is unacceptable, there is a pressing need for faster processing closer to the data source. However, as edge processing moves nearer to the point of data generation, it expands the attack surface, making systems more vulnerable to a range of security threats. The decentralized nature of edge computing complicates the implementation of uniform security measures across all the nodes [28]. The modern era of edge computing adopts a distributed framework, which necessitates effective resource management and secure task offloading to mitigate potential attacks and system failures. Security and privacy become critical in edge environments, especially when it is a resource-constrained environment. Processing sensitive data at the edge raises concerns about data confidentiality and user privacy. Ensuring secure data handling and storage at the edge is critical to prevent unauthorized access and data breaches [29]. Given the inherent challenges in edge computing, security, and privacy must be integrated as core components of the system architecture, as emphasized by several studies summarized in Table 2. In addition to these, persistent issues such as data minimization, infrastructure security, data anonymity, integrity, authorization, authentication, access control, and confidentiality continue to demand effective solutions. The emergence of advanced technologies such as 6G, AI, blockchain, digital twins [30], and edge intelligence has further expanded the research scope, offering new opportunities to design and implement efficient security protocols tailored for edge computing environments. Security solutions that operate in isolation are not sustainable in the long term, as they tend to increase the attack surface and make the system more vulnerable, ultimately eroding trust in the overall security framework. Moreover, such fragmented approaches can lead to decreased performance efficiency and higher operational costs. A more effective strategy in conjunction with the principles of Security-by-Design (SbD) [38]. This integrated approach ensures end-to-end security throughout the lifecycle of a device or system, enhancing trust, improving performance, and supporting policy compliance from the ground up. Hardware-Assisted Security (HAS) that facilitates SbD by enabling the integration of security mechanisms at the design level through dedicated hardware compo
###### Abstract  
Forest fires constitute a major environmental problem in tropical countries, especially in the context of climate change and increasing human populations. This paper aims to identify the causes of frequent forest fires in Son La Province, a fire-prone and forested mountainous region in northwest Vietnam, with a view to constructing a forest fire-related database with multiple layers of natural, social and economic information, extracted largely on the basis of Landsat 7 images. The assessment followed an expert systems approach, applying multi-criteria analysis (MCA) with an analytical hierarchy process (AHP) to determine the weights of the individual parameters related to forest fires. A multi-indicator function with nine parameters was constructed to establish a forest fire risk map at a scale of 1:100,000 for use at the provincial level. The results were verified through regression analysis, yielding R\({}^{2}\) = 0.86. A real-time early warning system for forest fire areas has been developed for practical use by the relevant government authorities to provide more effective forest fire prevention planning for Son La Province.  
forest fires; multi-criteria; analytic hierarchy process; the fire triangle; disaster mitigation; remote sensing +
Footnote †: journal: Applied Sciences