esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, obtaining accurate soil moisture data in remote agricultural areas can take time due to logistical and financial constraints. The European Centre for Medium-Range Weather Forecasts (ECMWF) provides global soil moisture estimates through the ERA-5 dataset,which offers hourly soil moisture data at different depths, facilitating the assessment of soil water contents across large areas. However, validating these remote sensing data with ground measurements is necessary to ensure their reliability. Field-based sensors can measure soil moisture at multiple depths (e.g., 10 cm, 30 cm, and 60 cm) and validate the remotely sensed soil moisture data in this context. These sensors enable an accurate understanding of soil moisture dynamics, particularly in regions where advanced irrigation practices still need to be fully implemented and where large-scale monitoring infrastructure is limited. Along with soil moisture measurements, crop water stress is influenced by evapotranspiration, which refers to the combined process of water evaporation from the soil and plant transpiration. ET is a key component in irrigation management, as it reflects the water lost from the production system and helps estimate crop water needs. The FAO Penman-Monteith equation is widely regarded as the most reliable method for calculating the reference evapotranspiration (ETo). Still, alternative strategies, such as the Hargreaves-Samani and Priestley-Taylor models, are also commonly used, particularly in areas where meteorological data are sparse [29; 30]. While these models differ in their approach, they are all helpful in estimating ETo and understanding crop water requirements. To address all the abovementioned limitations, this study seeks to integrate satellite remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation practices in avocado orchards in a tropical agricultural environment. By combining vegetation indices derived from multispectral imagery with soil moisture data collected at critical depths, this study aims to comprehensively understand water dynamics and crop stress. This is done by focusing on (i) vegetation monitoring through the use of indices such as the NDVI, SAVI, and NDWI to capture vegetation health, growth stages, and canopy water contents; (ii) soil moisture assessment by deploying sensors at varying depths to monitor the soil water content within the critical root zone and validate remotely sensed data; and (iii) irrigation optimization by estimating crop water requirements using reference evapotranspiration models and crop coefficients derived from vegetation indices. Our goal is to demonstrate how remote sensing, combined with ground-based soil moisture sensors and evapotranspiration models, can offer an innovative and practical solution for optimizing irrigation practices. The outcomes of this study will contribute to improving irrigation strategies in avocado orchards and provide insights that can be applied to other crops in water-scarce regions, thereby supporting sustainable agricultural practices worldwide.
# The Use of an Exact Algorithm within a Tabu Search Maximum Clique Algorithm
## 2 Exact Algorithms with a Coloring Bound
### Initial Evaluation of Exact Algorithms with a Coloring Bound  
Improvements to the Carraghan and Pardalos algorithm (denoted as \"C&P\") using a coloring bound are of three basic types. The first, denoted as \"_local coloring_\", applies a coloring algorithm to the original subproblem and to each new set \(S^{\prime}\) created as the depth of the search tree is increased. Clearly the coloring algorithm has to be very fast. The second, denoted as \"_start coloring_\", applies the coloring algorithm to the original subproblem, and the color assigned to each vertex is inherited by that vertex in the set \(S^{\prime}\). The number of colors actually used in \(S^{\prime}\) can then be computed to give a (usually weaker) upper bound. The third, denoted as \"_start and local coloring_\", is a hybrid. Start coloring and inheriting takes place as in \"_start coloring_\", but, if the upper bound obtained does not lead to pruning, then local coloring also takes place. This may give a smaller lower bound that does lead to pruning.  
The coloring algorithm used is a simple greedy algorithm. The vertices of the set are colored in turn, and each vertex is colored with the smallest available color. Two variations of this are applied to start coloring only. The _saturation degree_ of a vertex is obtained as follows. Once a new vertex has been colored, the saturation degree of an uncolored vertex is the number of colors in its neighborhood. Vertices are then colored in non-increasing order of saturation degree. This is denoted as \"_saturation start_\". The second very similar variation applied to start vertices uses Degree of Saturation (DSatur) coloring [15], itself based on saturation degree, but using a degree ordering to select the first vertex and to break ties. This is denoted as \"_DSatur start_\".  
Another option that can be used is to order the vertices of each new set \(S^{\prime}\) (created as the depth is increased) in non-increasing order of color. This means that, as subsequent vertices of \(S^{\prime}\) are considered, the coloring upper bound tends to decrease quickly and proves particularly effective. This option is denoted by \"_ordered_\" in the following tables.  
There is an important difference between the evaluation here and the general evaluation in [4]. Within HTS, many of the calls to the exact or pseudoexact algorithm supply a good lower bound. Thus, the evaluation here is carried out with the best available lower bound supplied. The time in seconds to complete the algorithms for a number of DIMACS instances [16] used in [4] are shown in Tables 1 and 2. No single algorithm is the best for all instances, but clearly vertex ordering is particularly helpful.
# O-Band Multimode Interference Coupler Power Combiner Using Slot-Waveguide Structures
###### Abstract
However, a low index material such as silicon nitride (SiN) can be a great solution because of its good properties [34; 35; 36] such as a low refractive index, low absorption at the O-band range, and low thermal sensitivity. The index contrast between silicon and SiO2 or SiN and SiO2 has a great influence on the waveguide guiding properties [37]. The index contrast in silica cladded silicon is around 145% and this index contrast does not change significantly with the signal shift. In a silica cladded SiN platform, the index contrast is around 35% at the O-band range. The benefits provided by the higher index contrast of the silicon platform as compared to the SiN platform come with the tradeoff of enhanced sensitivity of silicon devices to nanometer-scale geometrical variations. These can come from the thickness variation of the silicon layer or errors in the fabrication process, which means variations in width and etch depth of the waveguides. All these results can cause effective index deviations from the desired parameter value, which is a critical key parameter for the MMI coupler device. Therefore, it is better to use a SiN platform for the MMI coupler that functions at the O-band window. In this study, we propose a new design of a 2 \(\times\) 1 MMI power combiner based on SiN slot waveguide structures that operate on the O-band spectrum under a TE polarization mode. Optimizations of the key structure geometrical parameters were done using the full-vectorial beam propagation method (FV-BPM) and the finite difference time domain (FDTD) method for obtaining high power confinement, self-imaging, and higher-level power. Input tapers and the output taper were integrated into the MMI coupler for reducing the back reflection losses. This study shows how to design a combiner which can combine two coherent laser sources operating in the O-band range, which can achieve better performance results for the transmitter systems.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 5 Evaluation Experiment and Analysis
### Comparative Analysis of Methods
Compared with the traditional evaluation methods, the grey-target model based on improved principal component is more suitable for the evaluation of satellite attitude control system tracking mode.
# Threatening URDU Language Detection from Tweets Using Machine Learning
## 4 Experiments and Results
### Results Using Cross-Validation  
Cross-validation is another testing technique employed in this study. In independent set testing, just one split is done. However, sometimes the split might divide the biased data into train and test, which leads to underfitting or overfitting. To overcome this problem, cross-validation testing has been employed. Cross-validation is robust testing, and different folds are used for validation. In this study, testing is carried out using 5-folds and 10-folds. Five-folds correspond to the division of data into five folds, where the first fold is used for testing while the remaining four folds are used for training. In the next iteration, the second fold is used for testing, while the first and last three folds are used for training. This iteration continues until all five folds are used for testing. Finally, the average accuracy is computed for the individual accuracy from each fold.  
Results given in Table 11 indicate that SVC achieves the best precision and recall scores of 78.20% each. The best accuracy is obtained by the stacking model, i.e., 73.50%, while the highest F1 score of 73.80% is achieved by the ET classifier. ROC-AUC curve for 5-fold cross-validation is shown in Figure 6. It shows that the performance of the proposed stacking model is superior to other machine learning models employed in this study.  
Similar to 5-fold cross-validation, 10-fold cross-validation splits the data into 10 folds, where one fold is used for testing while the remaining nine folds are used for training. Ten iterations are needed for 10-fold cross-validation. In the end, the average accuracy is obtained using the accuracy for the individual fold.  
The results given in Table 12 show that the best performance in terms of accuracy is provided by the proposed stacking model, which obtains 73.90% accuracy using 10-fold cross-validation. Similarly, the best F1 score of 73.90% is also obtained by the stacking model. On the other hand, the best precision and recall are obtained by the SVC. Figure 7 shows the AUC-ROC curve using 10-fold cross-validation. Results indicate that the proposed stacking model outperforms all other models with an AUC of 0.806.  
Figure 5: Confusion matrices, (**a**) BoW-based results, and (**b**) TF-IDF-based results.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **SVC** & **Ext** & **Stacking** & **BNB** & **LR** \\ \hline Accuracy & 72.50 & 73.00 & 73.90 & 72.70 & 72.60 \\ \hline Precision & 78.60 & 73.90 & 73.50 & 69.00 & 75.40 \\ \hline Recall & 78.60 & 73.90 & 73.50 & 69.00 & 75.40 \\ \hline F1 score & 72.30 & 73.00 & 73.90 & 72.60 & 72.50 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of 10-fold cross-validation.  
Figure 6: ROC-AUC using 5-fold cross-validation.  
Figure 7: ROC-AUC using 10-fold cross-validation.
## 3 Application in Age-Development fMRI Data  
We provide a demonstration of the efficacy of our proposed smoothness harmonics in capturing age-related variations in cortical dynamics using fMRI data. Our spatiotemporal smoothness harmonics surpass conventional static functional connectivity patterns, offering a more comprehensive understanding of cortical dynamics in children and adults that reflects their developmental changes in cognition.
## 3 Unit Cell Analysis
### Elementary Analysis  
The electromagnetic properties of the unit cell are retrieved using Equations (1)-(9) provided above, which are calculated using \(S_{11}\) and \(S_{21}\). Figure 3a shows the current density of the proposed  
Figure 2: Magnitude of S-parameters (reflection co-efficient \(S_{11}\) and transmission co-efficient \(S_{21}\)) of proposed unit cell.  
LHM unit cell at a frequency of 8.4 GHz. The current flows mostly over the outer stripe. In each stripe, the current flows in the opposite direction due to the modified structure of the SRRs. The opposite current of the inner and outer stripes creates the stop band. From Figure 3b the negative permeability (\(\upmu\)) of the reported unit cell from 7.9 GHz to 12.3 GHz can be seen. The negative permittivity (\(\varepsilon\)) of the proposed MTM unit cell from 2.85 GHz to 4.84 GHz and 7.9 GHz to 10.05 GHz is shown in Figure 3c. The negative refractive index will appear if the permittivity and permeability of a unit cell remain negative simultaneously. Figure 3d demonstrates the effective refractive index (\(\upmu\)) from 7.9 GHz to 14.2 GHz. The permeability, permittivity and refractive index have negative peaks at 7.9 GHz, which represents the proposed unit cell as having negative metamaterial characteristics. From the characteristics, the LHM structures improved remarkably compared to recently-published metamaterials, including negative values over broad band. The tuning of both copper strip rings and the gap between them is used to attain the band gaps around the resonance or unattainable stop band.
## 5 Security Analysis and Discussion
### Security Properties Analysis  
We consider both type I and type II adversaries. A type I adversary does not know the master key of KGC but may replace the public key of an arbitrary entity with a value of its choice, whereas a type II adversary knows the master key but cannot replace the target entity's public key.  
(1) Security of Key Distribution  
Even during the distribution process, a malicious attacker can obtain the partial key \((s_{IG},R_{IG})\) of \(IG\). Since attackers cannot obtain the secret value \(x_{IG}\), which is chosen randomly by \(IG\), they cannot complete the push process from the partial key \((s_{IG},R_{IG})\) to the complete key \((s_{IG},x_{IG})\). Therefore, we can ensure security of key distribution.  
(2) Perfect Forward/Backward Security  
Irrespective of whether the intelligent gateway node joins or leaves, the group key \(M_{<0,0>}\) will change due to the particularity of the logical key tree structure. Therefore, there is no doubt that the disclosure of the current session key will not lead to the disclosure of the previous session key, nor will it pose a security threat to the subsequent session key.  
(3) Resist Private Key Disclosure  
When the private key \((s_{IG},x_{IG})\) of each intelligent gateway node is obtained by a malicious attacker, the malicious attacker can compute \(K^{1}_{ij}\); however, the malicious attacker cannot compute \(K^{2}_{ij}\) and \(K^{3}_{ij}\) without \(k_{1}\) and \(k_{2}\) to obtain the group key.  
(4) Resist Temporary Information Disclosure  
Even if both nodes leak \(k_{1}\) and \(k_{2}\) at the same time, the malicious attacker can compute \(K^{3}_{ij}\); however, the malicious attacker cannot compute \(K^{1}_{ij}\) and \(K^{2}_{ij}\) from \(K^{3}_{ij}\) to obtain the group key \(M_{<0,0>}\).  
(5) Resist the MITM Attack  
An adversary A may try to modify the message that \(IG_{1}\) send to \(IG_{2}\). However, the message is encrypted through the session key \(M_{<0,0>}\). If A wants to complete the modification, A needs to know the private keys of all nodes, which is difficult.  
(6) Resist the Eavesdropping Attack  
An adversary A may intercept the messages exchanged in the communication link. However, the communication between any two nodes will be encrypted through the session key \(M_{<0,0>}\). When A does not know the private keys of all nodes, it is impossible to directly calculate the session key.
## 2 Methods
### Training Dataset and Hyperparameters  
Using the SADCGANs to achieve the vector decomposition of P- and S-wave modes requires preparing a set of training data for training the neural network. In our work, we first simulate the coupled elastic seismic wavefields at different times for a given source position through forward modeling based on the elastic wave equations for different isotropic elastic models, and then randomly select some of these coupled elastic seismic wavefields. Next, we use the horizontal and vertical components of the decoupled vector P- and S-wave modes for these selected elastic seismic wavefields separated by the vector decomposition algorithm in the wavenumber domain as training samples, and further use those unselected elastic seismic wavefields and the elastic seismic wavefields for other source positions as testing samples. During training, the coupled elastic seismic wavefields \((u_{x},u_{z})\) are fed as input samples to the generator, which outputs the generated horizontal and vertical components of the decomposed vector P-wave mode \((u_{xP},u_{zP})\). Next, the discriminator compares two generated components with two real components for vector P-wave mode, and it outputs a value between 0 and 1 that indicates the probability of generated samples being real. Once the discriminator determines that the generated samples are real, the horizontal and vertical components of the decomposed vector P  
Figure 3: The network structures of generator (**left**) and discriminator (**right**) for SADCGANs which are used in our work.  
wave mode (\(u_{xP},u_{zP}\)) can be outputted, and the horizontal and vertical components of the decomposed vector S-wave mode (\(u_{xS},u_{zS}\)) can be obtained using Equation (3).  
Our work's goal is to effectively and accurately separate the horizontal and vertical components of the decoupled vector P- and S-wave modes from the coupled elastic seismic wavefields. To better train the neural network, the appropriate hyperparameters are critical considerations. In our work, we carry out many experiments to select the optimal hyperparameters, including training epochs and learning rate. After repeated experiments, the Adam optimizer with 400 training epochs and a learning rate of 0.0002 is used to train the neural network, and the learning rate remains equal for the first 200 epochs and then gradually decreases to zero over the next 200 epochs.
## 5 Discussion
### Robustness to Channel Noise  
The imaging performance of all methods under different channel SNRs in the simulation was studied to evaluate the robustness to channel noise. As seen in Figure 9, CMSB-based methods all obtain an improved resolution, contrast, gCNR, and sSNR under different channel SNRs compared with DMAS. In addition, the contrast, gCNR, and sSNR obtained by CMSB-based methods degrade with the decreasing channel SNR, whereas the resolution increases. This is because the subarray lengths selected for speckle signals and clutter decrease as the channel SNR decreases, which results in contrast degradation and mainlobe interference rejection. Under the channel SNR of 5 dB, the contrast and sSNR obtained by LOCw-CMSB (\(\delta=\delta_{ac}\)) are both much higher than that of DMAS and CMSB. This indicates that the contrast performance of LOCw-CMSB (\(\delta=\delta_{ac}\)) is less susceptible to the channel noise, and the speckle quality can be preserved under a high level of channel noise.  
(\(\delta=\delta_{ac}\)) improve gCNR and sSNR as the kernel size increases, but the contrast degrades. In addition, the computational efficiency of LOC estimation decreases with the increasing kernel size. Hence, the kernel size in the range of 3\(\lambda\) to 7\(\lambda\) is appropriate for LOC weighting to obtain a high contrast while preserving the speckle quality to some extent, as well as avoiding a high computational load.
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 3 Blockchain Platforms
### IoTeX Blockchain  
The IoTeX blockchain platform was custom-built to cater to the specific needs of the IoT ecosystem. It was developed to overcome the obstacles and constraints faced by current blockchains in meeting the distinct demands of IoT devices and applications. The IoTeX blockchain platform encompasses several critical facets and elements [59].  
The architecture of IoTeX consists of multiple interconnected blockchains, including the Root Chain and multiple sub-chains customised for specific use cases. The Root Chain serves as the main blockchain responsible for network consensus and security. The consensus algorithm employed by IoTeX is Roll-DPoS, which enhances security, scalability, and decentralisation. Various token standards, such as IOTX, XRC20, and XRC721, are supported by IoTeX, and tailored for IoT use cases [59].  
IoTeX achieved high scalability using sub-chains, which allows for parallel processing and reduces congestion on the main chain. The platform also incorporates advanced privacy features like secure data transfers and lightweight encryption. A decentralised identity framework is offered by IoTeX, which enhances security and control over personal data. The Internet of Trusted Things concept was introduced by IoTeX, focusing on building secure and trusted interactions between devices and users [59].  
IoTeX employs innovative cryptography methods to generate secure and unpredictable random numbers, which is essential for certain use cases in IoT. The platform is designed to be compatible with other blockchains, enabling cross-chain communication and interoperability. A growing ecosystem of developers, partners, and community members is working together to build and expand the platform's capabilities and use cases.  
The specialised focus of IoTeX on IoT and its architecture were designed to address IoT-related challenges and distinguish it from other general-purpose blockchain platforms. The platform strives to provide a secure and scalable foundation for various IoT applications, including supply chain management and data marketplaces [60]. [PERSON] et al. [61] found that works on IoTeX have shown the strength of the technology in addressing the key challenges in integrating IoT with blockchain. [PERSON] and his team published a paper after developing a user-centric Blockchain-based secure IP camera, which was built on the IoTeX platform. Other works were seen from different domains, such as Wearables [62] and Mobile Payment [63].
# The Effects of 3D and 2D Imaging on Brain Wave Activity in Laparoscopic Training
## 3 Results
### Mental Workload
#### 3.2.2 EEG Gamma Wave Activity  
There were no significant main effects of the tasks on EEG gamma wave activity (\(F_{1,11}=0.243\), \(p=0.632\)). The EEG gamma wave activities were 1711.96 (758.40) Bel-Seconds for circle-tracing and 1649.38 (849.40) Bel-Seconds for peg transfer. In terms of vision systems, the EEG gamma wave activity was significantly higher for 2D than for 3D (\(F_{1,11}=19.847\), \(p=0.001\)). The EEG gamma wave activity was 2027.75 (865.15) Bel-Seconds for 2D and 1333.58 (546.62) Bel-Seconds for 3D.  
A significant interaction of simulated task \(\times\) vision system (\(F_{1,11}=6.205\), \(p=0.030\)) was found. For the interactions of task with EEG gamma wave activity and vision system, the results of one-way ANOVA indicated that EEG gamma wave activity was significantly higher in the 2D than in the 3D condition in both the circle-tracing (\(F_{1,11}=7.585\), \(p=0.019\)) and peg transfer (\(F_{1,11}=23.046\), \(p=0.001\)) tasks.