esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Protocol
### Authentication Mechanisms
#### 3.4.1 Public Key Infrastructure (PKI)  
PKI is a widely used authentication mechanism that utilizes digital certificates to verify the identity of network entities. In PKI, each entity has a public-private key pair, and the public key is distributed through a certificate authority (CA). The CA signs the certificate using its private key, which is trusted by all entities in the network. When a node wants to communicate with another node, it verifies the other node's certificate using the CA's public key to ensure that the certificate is genuine and has not been tampered with. The authentication process in PKI can be represented using the following Equation (1):  
\[V(\text{Ku\_CA,Cert\_A})=1 \tag{1}\]where \(V\) is the verification function. \(Ku\_CA\) is the CA's public key. \(Cert\_A\) is node A's certificate.
## 7 Conclusions and Future Work  
In this paper, we discussed the EVCS security requirements, threats, and challenges. We compared different approaches used in the literature to address the different types of threats affecting the EVCS/IoT systems. We also presented the usage of the various datasets in recent research papers. We applied machine learning methods to be used in anomaly-based IDSs with high accuracy. We evaluated two traditional classifiers used in Machine learning: The Decision Tree Classifier and the Filtered Classifier. The Filtered Classifier has the best performance in binary classification in terms of accuracy, precision, recall, F-1 score, and modeling time, making it the best solution for identifying DDoS attacks in the IoT Environment. These results are in line with those of previous studies. The suggested method may be used to enhance the security of any sensitive Industrial Control Systems (ICSs), ranging from SCADA to green hydrogen control systems. By highlighting the need for training classification models on appropriate datasets, we hope that the results of this research will contribute to the development of a complete IDS. In this paper, we minimized the training data used in the simulations and observed the accuracy of the intrusion detection system. The results proved that the detection accuracy was not affected. This should help in identifying malicious traffic with minimal training data, which increases the agility of the EVCS IDS. In the future, we plan to investigate the role of feature selection and other deep learning techniques. We will focus on the Deep Learning algorithms, and various datasets will be used to assess IDS efficiency.  
Methodology, M.E.; Validation, M.A.A.; Formal analysis, M.E.; Resources, M.S.E.; Writing--original draft, M.E.; Visualization, M.A.A.; Supervision, H.A., M.S.E. and A.D.J.; Funding acquisition, A.D.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Data Availability Statement:** Dataset used can be downloaded from [[https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)]([https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 4 Validation Activities at Reference Sites
### Other Validation Experiments  
Validation activities were also performed in study sites other than HiWATER. A typical study included observing fluxes using EC and LAS systems to validate the ET product in the Hai River Basin, across typical underlying surfaces of the northern mountains (Miyun), croplands in the central suburbs (Daxing) and croplands in the southern plains (Guantao) [66,67]. [PERSON] et al. [67] proposed an innovative validation method based on multi-source ET measurements. The validation results included accuracy assessment, analysis of error source and uncertainty within the validation process at the basin and local scales. Both scales showed a good agreement between the estimated and observed ET. [PERSON] et al. [74] investigated the effect of footprint characteristics for validating satellite-based surface fluxes in two river basins. The results showed that the footprint is crucial in defining a consistent spatial scale between ground measurements and satellite-based surface flux estimates in validation, particularly for heterogeneous surfaces and high-resolution remote sensing data.  
A study of accuracy assessment of multi-scale LAI observations occurred in a meadow steppe of Hulunber, Inner Mongolia, China [86]. Datasets involved were ground-based measurements, MODIS CS LAI and land cover type products with 1 km and 500 m resolution, respectively, as well as LAI maps derived from Environment and Disaster Monitoring Small Satellite Constellation of China (HJ)-1A/1B CCD images with 30 m resolution. A slight overestimation in the averaged magnitude of 0.4 was found for the MODIS LAI product compared with the HJ-retrieved LAI maps, and the relative absolute errors of the product ranged from 10% to 50%. [PERSON] et al. [149] introduced a concept of mean length variability to be used as an index to quantify spatial heterogeneity over croplands for multi-temporal NDVI, near-infrared, and red reflectance. This investigation suggested that the spatial heterogeneity varied with the changes in the fraction of vegetation cover, and a spatial resolution larger than 120 m could effectively limit the difference of spatial heterogeneity among different remote sensing observation methods.  
In addition to these individual activities, other field campaigns and intensive observations have been performed, particularly at anchor sites within a national validation network being coordinated (see Section 5). As a result, we are highly confident that the validation experience learned from local-scale validation is rewarding to leverage validation exercises to be practiced at larger scales.
## 3 Analysis of SSO Inhibition Mechanism in the DFIG System
### Back-Stepping Control Design Based on DFIG Decoupling Model
In the above equation, \(k_{2}\) is the control gain, and the Lyapunov function is constructed according to Equation (4). \[\lambda_{2}=\frac{1}{2}e_{2}^{2} \tag{15}\]  
In accordance with Equation (8), we can take the derivative of Equation (15), and substitute Equations (13) and (14) into it:  
\[\dot{\lambda}_{2}=-k_{2}e_{2}^{2}\leq 0 \tag{16}\]  
It can be seen that \(\lambda_{2}\geq 0\) and \(\dot{\lambda}_{2}\leq 0\); the subsystem is stable and can control the rotor q-axis current \(\widetilde{i}_{\rm rg}\). Under the control rate described by Equations (10) and (14), the following equation can be obtained using Equations (9) and (13):  
\[ \begin{cases}\dot{e}_{1}=-k_{1}e_{1}\\ \dot{e}_{2}=-k_{2}e_{2}\end{cases}  \tag{17}\]  
The Lyapunov function is constructed from Equations (4) and (17):  
\[\lambda=\frac{1}{2}e_{1}^{2}+\frac{1}{2}e_{2}^{2} \tag{18}\]  
In accordance with Equation (5), the derivation of Equation (18) can be obtained:  
\[\dot{\lambda}=-k_{1}e_{1}^{2}-k_{2}e_{2}^{2}\leq 0 \tag{19}\]  
It can be seen from Equations (18) and (19) that, under the control of the control rate described by Equations (10) and (14), the RSC control system is stable. Therefore, the controller designed according to Equations (10) and (14) can meet the stability requirements for the DFIG system under oscillation. At the same time, the design of the control rate simplifies the control strategy for traditional RSC systems and reduces the number of paths of the oscillation input into the RSC control system. The SSO is only input into the RSC system through the power outer loop and the current inner loop and the sub-synchronous component does not need to be considered in the feedforward decoupling compensation amount.
## 4 Results and Discussion
### Observations on the Temporal and Spatial Resolution Requirements for SAR Rice Crop Mapping  
Despite good local knowledge and planning, the acquisitions did not always coincide with the rice crop calendar, resulting in significant areas of late or early rice detection. The complexity of cropping calendars, changes in farmer intentions, unexpected delays to the start of the season and delays in the release of irrigation water all play a role in this. Acquisition plans for rice crop monitoring need to account for this uncertainty in rice calendar information. Year-round monitoring may be needed in areas where rice is cultivated several times a year.  
Appropriate temporal frequency plays a key role in the accurate detection of rice area and in other rice crop growth information. For short- and medium-duration crops, as grown in nine of the 13 footprints, the season duration is approximately three to four months, which translates into 6-8 CSK images or 9-12 TSX images per season. For these durations, we found that at least 6-7 images are needed to perform a reliable analysis, and this suggests that a 16-day repetition is the minimum requirement for rice crop monitoring using this approach. Higher temporal frequency would of course be better considering the fastdevelopment of the rice plant and the sensitivity of the X-band to the different crop stages. Future work should explore the derivation of key crop dates and stages from the same series of images.  
High spatial resolution is also important and is related to field size, planting homogeneity and landscape fragmentation. In areas characterized by large paddy fields in homogeneous clusters, lower resolution data could be suitable. The Red River Delta and northeast Thailand are good examples of this rice environment. However, in areas, such as Leyte in the Philippines, where most plots are smaller and where neighboring fields can have completely different crop stages, a higher spatial resolution is desirable to capture rice area and other rice crop information.  
Regular acquisitions with high spatial and temporal resolution over a large area will require substantial processing capability, such as scalable cloud computing solutions or the use of high-performance computing clusters for parallel processing. The basic processing chain in MAPscape-RICE (see Section 3.1) to convert SLC data into terrain-geocoded \(\sigma^{\circ}\) values is already fully automated and highly suitable for parallel processing, but the rice map classification requires operator decisions on appropriate parameters. A library of parameters for different environments and management situations could increase the efficiency of the classification process.
## 4 Real Example  
Bacchus is a project that has been developed with the purpose of carrying out agricultural inspection and harvesting efficiently. In collaboration with Robotnik, the RB-VOGUI+DUAL mobile robot has been implemented, equipped with two integrated UR robotic arms. This system incorporates an advanced set of sensors that allow for the collection of environmental data and detailed inspection of crops. During the harvesting phase, force sensors play a crucial role, ensuring the robots' precision in handling the fruits. In addition, adaptable grippers made using additive manufacturing techniques are used, specifically designed to handle the fruits delicately and without causing damage (Figure 5).
## 6 Simulation Results  
ETAP 20.6 software was utilized in this analysis to achieve the research objective. It is presumed that the temperature and the irradiance are varied. The climate condition assigned is a hot, cloudless day in the summer season. Characteristics of a PV system and battery parameters are analyzed. Solar irradiance is time-dependent; Figure 6 illustrates  
Figure 5: Flow chart—operation of energy sources [34].  
and evaluates the irradiance at a 15 min interim. Irradiance is directly proportional to the PV array performance. It can be seen in Figures 3 and 6 that when the irradiance increases, the power output also increases. If a solar PV encounters a deficiency in irradiance, its output power is at its lowest performance, leading to the inability to provide power to the load in the MG. Correspondingly, the battery will remunerate the needed power to be supplied to the load. In addition, the battery will continuously remunerate the load until the PV output power is sufficient and until it reaches its discharge limit, whichever comes first.  
The daily profile of the load is hereby depicted in Figure 7. It is apparent that considerable power consumption occurs in the morning, ascends from 05:30 to around 12:00, and the peak load is detected from 16:00-20:00. The motivation of this peak load is substantiated by the time when most end-users are found to be utilizing power for various purposes. During this period, the sun has set, and power from the PV system is inadequate, hence, the requirement for a functional energy storage system.  
Figure 6: Photovoltaic irradiance.  
Figure 7: Daily load distribution profile.  
When the energy storage attains the limitation sooner than anticipated, the main grid will supply power to the load with the support of a power electronic interface. Solar irradiance is harnessed by the PV system between the period of 05:00 and 18:00. The solar irradiance's intensity fluctuates between 0 W/m\({}^{2}\) and 1000 W/m\({}^{2}\) as illustrated in Figure 6. The MG system's performance is as follows:  
The MG's energy sources (battery and PV system) are liable to deliver power to the interconnected variable DC load. The energy storage system is dependent on the excess power output from the PV system and power grid to be operative. This excess power must be greater than the variable load to be stored for later use. The BMS (Battery Management System) settings are set so that when the PV power output (DC bus) is less than that of the load demand, the battery will supply the remaining required power. This is conditional on whether the battery has already attained its cut-off charge or discharge limit. Therefore, the required additional power will be provided by the grid. It is recommended that the conducive cut-off state of charge (SoC) limitations are 20 and 80 percent [34]. This aids in preventing the battery from deep discharge and overcharging; hence, extending the battery's lifespan. The limitations are shown in Figure 8a,b. Table 3 depicts the glossary to comprehensively interpret Tables 4 and 5.
## Materials and Methods
### Statistical Analysis  
Calculations of alpha diversity indexes were performed in R versions 3.5.3 [42] with the phyloseq R package [49]. The Shannon and inverse Simpson indexes were compared among the variables since these indexes consider the richness and evenness that are powerful in providing insights into the structure of the microbial community [51; 52]. In addition, the number of ASVs (species) was estimated using the observed richness and Chao1 richness estimator. The alpha diversities among the groups of samples were statistically tested using the analysis of variance (ANOVA) test to evaluate any differences in the microbial composition among treatment types and sampling days, as both variables (treatment type and sampling day) had more than two levels and the data distribution was normal according to the Shapiro-Wilk normality test. [PERSON]'s honest significance test as a post hoc test was performed on the ANOVA results to compare within-group alpha diversity.  
Measurements of samples similarity (beta diversity) with the R phyloseq and vegan packages [53] were also performed at ASV level based on non-metric multidimensional scaling (NMDS) Bray-Curtis dissimilarity [54] and Unifrac distances [55], which include abundance and phylogenetic information respectively, in addition to taxon counts. Statistical significance testing among the groups, such as the type of sanitization and the days post sanitizing treatment, was performed using permutational multivariate analysis of variance (PERMANOVA) [56] using the adonis function in the R package vegan. The community pattern of microbial composition among the groups using taxon dissimilarity information was visualized by NMDS Bray-Curtis and Unifrac ordination methods. In addition, microbiota heterogeneity, a measure of dissimilarity of the beta diversity (Bray-Curtis) of each sample with respect to the group, was compared between the various types of treatment (sanitizing and control washes) and days of sampling to evaluate the differences in homogeneity of each treatment group and homogeneity of sampling day using the R package microbiome [57]. Statistical tests for multiple variables within the type of treatment and sampling days were performed by the betadisper function on distance matrix (Bray-Curtis), and an ANOVA was performed to compare the variances between pairs of groups using the permutest function by setting the pairwise variable to true and the number of permutations to 1000 on R package vegan [56].  
Analysis of differentially abundant taxa among the types of sanitization and days 0, 5, and 10 post treatment, at ASV and family level, were performed using analysis of composition of microbiomes (ANCOM) [58] plugin in QIIME 2 [59], at ASV and genus levels. For ANCOM analysis, ASVs present in less than three samples and ASV frequencies below fifty were removed before the analysis.
## 3 EDAS Prototype Specifications
### Architectural Overview  
From a structural point of view, EDAS is designed as a component-based architecture (CBA) [12], where the design is fragmented into functional components necessary for achieving adaptive security. The components interact with each other using provided (output) and required (input) interfaces whenever an event is generated. Hence, from a communication perspective, EDAS utilizes an event-driven architecture (EDA) [8] in which events generated by a monitored component (thing) trigger concerned components. Events are considered as security changes and are monitored and correlated using a complex event processing (CEP) method. A CBA and EDA design make EDAS a reusable, replaceable, extensible, interoperable and independent architecture.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## The Proposed Scheme
### Image Recovery  
Corresponding to the generation process of recovery watermark in Section 3.1.2, the image recovery process is also divided into two kinds of situations: recovery for smooth blocks and recovery for texture blocks.  
1. For tampered smooth block, the recovery watermark is the average pixel value of original block. To reconstruct the block, the recovery watermark is first extracted from its mapping block. After decryption and the binary-to-decimal conversion, we get the final recovery data.
2. For invalid texture block, the recovery watermark is the quantized DCT coefficients. According to the inverse process of the watermark generation process (Steps 1-3) given in Section 3.1.2, the recovery data for texture block can be obtained.  
All the recovery data are in the range from 0 to 31. To restore the block, the 5 MSBs of each pixel are padded with the recovery data, while its 3 LSBs are set as 0.  
For the blocks that are unsuccessfully restored, the average values of the valid pixels in its block neighborhood are used to replace the four pixels in the block. After the image recovery process, the tampered image can be restored with a good quality.
# DELER: Distributed Clustering with Local Direction Centrality and Density Measure
## 3 The DEALER Algorithm
### Overview  
As illustrated in Figure 8, the DEALER algorithm comprises the following steps: (1). Data Partitioning: The data are first sorted based on \(z\)-values and divided into tasks,  
Figure 6: Illustration of coordinate transformation.  
Figure 7: \(z\)-value coordinate representation.  
aiming to assign spatially close points to the same partition whenever possible. Additionally, precise quantile-based partitioning is employed to ensure load balancing among subnodes, thereby completing the overall data partitioning process. To facilitate the accurate identification of local K-nearest neighbor (KNN) points in later steps, data replication and range reduction are performed in advance. (2). Computation of _DCM_ and \(\rho\) for local clustering: Within each node, local clustering is conducted using two key metrics: _DCM_, which quantifies the centralization of data points, and \(\rho\), which represents data point density. These measures are combined to distinguish between boundary points and cluster centers. (3). Data Clustering: First, local clustering is performed within each node based on the previously computed directional centrality and density. Then, the initial clusters formed through local clustering are merged to ensure that clusters that should belong to the same group are unified, ultimately achieving the final global clustering outcome. The framework follows a natural logical flow, beginning with data partitioning, followed by intra-partition _DCM_ and density calculations, and concluding with data clustering. To align the framework with the chapter headings for better readability, both local and global clustering are categorized under data clustering. Additionally, since data partitioning involves certain steps and techniques related to intra-partition _DCM_ and density calculations, presenting the computational aspects first enhances clarity. Placing data partitioning at the beginning would necessitate referencing many computational details within that section, leading to a more cumbersome and lengthy explanation. Thus, for conciseness, the order of presentation has been temporarily adjusted.
## 3 Experiments and Results
### Effect of the Hyperparameter  
To evaluate the effect of the KD weight parameter \(\gamma\) in our algorithm, we conducted experiments on the MNIST data with top two classes per client for two models: a MLR model and a MLP model with \(\gamma=0.1,0.3,0.5,0.7,0.9\). For the MLR model, Figure 0(a) shows that average test accuracy stabilizes above 98% within 200 rounds, with \(\gamma=0.1\) and \(\gamma=0.3\), while \(\gamma=0.9\) fluctuates and plateaus around 94%. The corresponding training loss (Figure 0(b)) indicates that \(\gamma=0.1\) and \(\gamma=0.3\) converge to below 0.1 after 200 rounds, whereas \(\gamma=0.9\) retains a higher loss of approximately 0.28. In contrast, the MLP model exhibits more pronounced variability (Figure 2): accuracy initially surges but drops sharply for \(\gamma=0.9\) (below 90% after 100 rounds) before gradually recovering to around 92% by 800 rounds, while \(\gamma=0.1\) and \(\gamma=0.3\) maintain around 98% with slight fluctuations. The MLP's training loss decreases steadily for all \(\gamma\) values, but \(\gamma=0.9\) remains elevated (above 0.5), compared to lower losses (below 0.2) for \(\gamma=0.1\) and \(\gamma=0.3\). This behavior indicates that with high \(\gamma\), the MLP may over-adjust its weights to mimic the global model, ignoring local data patterns, which exacerbates the accuracy drop compared to the simpler MLR model. The MLP's deeper architecture makes it more sensitive to the choice of \(\gamma\), highlighting the need for careful tuning in non-IID settings. The results suggest that for deeper models like MLPs in non-IID settings, a lower \(\gamma\) (e.g., 0.1 or 0.3) strikes a better balance between local adaptation and global regularization, avoiding the instability seen with high \(\gamma\). These findings underscore the importance of model-specific tuning to mitigate instability in complex architectures under heterogeneous data distributions. Specifically, \(\gamma\)'s role in weighting the KL divergence ensures local models benefit from global knowledge without overfitting to the teacher, as evidenced by stable 98% accuracy for \(\gamma=0.1\).  
Figure 1: Effect of KD weight \(\gamma\) on the multinomial logistic regression (MLR) model using the MNIST dataset generated by Dirichlet distribution (\(\alpha=0.5\)) with top 2 classes over 800 global rounds: (**a**) Average test accuracy (%). (**b**) Average training loss.