esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
## 2 Features of a Reliable Underwater Communication System
### Implemented Receiver  
The structure of the FFH-FSK receiver implemented in the considered data transmission system is shown in Figure 4. Initially, the received signal \(r(t)\) is subjected to analogue-to-digital conversion, and then the selected band signal is reduced to the baseband of the specified FFH-FSK width. The next operations lead to a discrete signal spectrum, which is used to determine the power density spectrum of the discrete signal for subsequent \(T_{H}\) durations. The obtained spectrum covers the entire FFH-FSK band, i.e., it covers all possible branches corresponding to the significant frequencies of FSK modulation for each of the \(L\) possible hops, hence the number of all branches analyzed is \(L\cdot M\). Next, the receiver extracts the values of the spectral lines \(Y_{m,k}\) and determines the decision values \(Z_{m}\) according to the selected reception diversity algorithm (\(m=0,\ldots,M-1;k=1,\ldots,L\)), in pursuance of the assumptions of soft-decision decoding. The basic condition for the proper functioning of the FFH technique receiver is its synchronization with the frame of the transmitted symbol, and then the algorithm is zeroed and all decision variables \(Z_{m}\) are worked out.  
In the first stage, for the next \(L\) hop times \(T_{H}\) that occur for transmissions of a single symbol, the values of the \(Y_{m,k}\) bands are extracted for each of the \(M\) FSK branches and \(L\) of possible hops, creating a matrix with dimensions \((M+L)\cdot L\). In the second stage, based on the obtained matrix, the operation of focusing the spread spectrum symbol signal is performed by selecting the value of the spectral lines from \(M\)-th FSK branches for the next \(L\) hop times \(T_{H}\). Transmissions of a single symbol are \(L\) hops, and the next hop \(k\) is in accordance with the hop pattern determined based on the fixed code sequence. As a result of these actions, a matrix of values \(W_{m,k}\) with dimensions \(M\cdot L\) is created, which is used by the reception diversity algorithm to determine the decision value of \(Z_{m}\), independently for each branch of \(m\). The obtained decision values of \(Z_{m}\) allow the final decision to be made, i.e., to assign the appropriate FSK symbol to the obtained decision values \(Z_{m}\).  
Figure 4: Structure of the implemented FFH-FSK receiver.  
The square-law combining algorithm was used as the reception diversity algorithm in the FFH technique, which sums \(L\) values of \(W_{m,k}\) from each MFSK branch, according to Formula (5). It assumes that independent transmission channels have the same gain [23].  
\[Z_{m}=\sum_{k=1}^{L}{W_{m,k}}^{2} \tag{5}\]
## 3 Results and Discussion
### Preparation and Characterization of the Materials
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample** & **MB Saturation Volume (mL)** & **CEC (mEq-100 g\({}^{-1}\))** & **CEC SSA 1 (m\({}^{2}\)-g\({}^{-1}\))** & **BET SSA 2 (m\({}^{2}\)-g\({}^{-1}\))** & **Pore Size (Å)** \\ \hline Ka & 0.40 & 8.0 & 62 & 15 & 179 \\ M-Ka & 0.15 & 3.0 & 23 & 69 & 110 \\ Zeo & 0.40 & 8.0 & 62 & 2 & 355 \\ Ka-R & 0.30 & 6.0 & 47 & 16 & 184 \\ M-Ka-R & 0.10 & 2.0 & 16 & 79 & 113 \\ Zeo-R & 0.25 & 5.0 & 39 & 1 & 1130 \\ \hline \hline \end{tabular}  
* From CEC values obtained from MB adsorption; \({}^{2}\) From N\({}_{2}\) adsorption at \(-\)196 \({}^{\circ}\)C.  
\end{table} 
Table 4: CEC and BET specific surface areas for the precursors Ka, Ka-R, M-Ka, and M-Ka-R; and the final zeolites Zeo and Zeo-R.  
Figure 4: SEM micrographs of purified white kaolin (**a**) and red kaolin (**b**), Zeo zeolite (**c**,**d**) and Zeo-R zeolite (**e**,**f**).
## 3 Fault Diagnosis of the Protection System Based on Gradient Boosting Decision Tree (GBDT)
### Training of Fault Diagnosis Model Based on Gradient Lifting Tree
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fault Types** & **Number of Samples** & \multicolumn{3}{c}{**Number of Training**} & \multicolumn{3}{c}{**Number of Test**} \\  & & & & **Samples** & & **Samples** \\ \hline \(f_{1}\) & 351 & 316 & 35 \\ \(f_{2}\) & 371 & 334 & 37 \\ \(f_{3}\) & 347 & 312 & 35 \\ \(f_{4}\) & 344 & 310 & 34 \\ \(f_{5}\)- & 345 & 310 & 35 \\ \(f_{6}\) & 353 & 318 & 35 \\ \(f_{7}\) & 352 & 317 & 35 \\ \(f_{8}\) & 348 & 313 & 35 \\ \(f_{9}\) & 346 & 311 & 35 \\ \(f_{10}\) & 354 & 319 & 35 \\ \(f_{11}\) & 340 & 306 & 34 \\ \(f_{12}\) & 349 & 314 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Distribution of fault samples. It can be seen from Table 8 that the GBDT algorithm has the highest accuracy compared with the other two algorithms when dealing with the same dataset due to its excellent performance on small sample sets, and GBDT has fewer iterations in training, faster-running speed, and training process. To explore the influence of the number of samples in the training set on the accuracy of the model and compare the accuracy of the three methods, according to the distribution ratio of fault samples in Table 5, the number of samples in the training set is changed for training, and the test results are shown in Figure 5  
Figure 5 shows that when the number of samples in the training set reaches 3800, the accuracy of the model reaches 99%, and with the increase in the number of samples in the training set, the accuracy of the model does not improve much. Therefore, in practical application, higher accuracy can be achieved when the number of samples in the training set reaches 3800.
## 6 Solution Types/Algorithms
### Particle Swarm Optimization Based Algorithms  
Particle swarm optimization (PSO) was developed in 1995 [176] which was based on swarm behavior such as fish and bird schooling in nature. Due to its peculiar structure, the intelligence does not reside in the individuals rather it is distributed among a group of many individuals. PSO has gained immense popularity in recent years and has been used in several research articles to solve different optimization formulations. For example in [60], a multi-objective optimization formulation has been used to obtain energy efficient clustering and routing algorithms for wireless sensor networks. The proposed algorithm was based on particle swarm optimization approach to achieve a trade off between network life time, energy consumption, dead sensor nodes and delivery of total data packets to the base station. In [91], the authors have suggested to use a multi-tier clustering approach using cultural-based multi-objective particle swarm optimization to maximize the life time of the wireless sensor networks. A cooperative spectrum sensing technique in cognitive radio network has been proposed in [177] which exploited multi-objective hybrid invasive weed optimization and particle swarm optimization. This soft decision fusion technique was suggested to optimize the global decision threshold and weight coefficient vector was assigned to each cognitive users to facilitate maximization of detection probability and minimization of false alarm probability and overall probability of error at the same time. A dynamic sensor network management technique using multi-objective particle swarm optimization has been proposed in [178]. The output of the algorithm was the selection of sensors, threshold of the individual sensor and optimal decision fusion rule. A multi-objective optimization approach for sensor network management through fitness function design has been suggested in [179], by using a particle swarm optimization. The authors argued that the swarm can be designed to reduce run time for real-time applications as well as improving the performance of the system. In [180], a multi-objective discrete particle swarm optimization for multisensor image alignment has been proposed to obtain global best match points. The intermittent fault detection in wireless sensor networks is formulated as a multi-objective optimization problem [181]. The problem is solved by using a PSO based algorithm to achieve a trade off between inter test interval and maximum number of tests required to diagnose the node failure.
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 4 Discussion
This inconsistency can reduce the accuracy of the machine learning model and introduce potential errors in the prediction results [38]. Furthermore, while multimodal data provide valuable insights, they still lack comprehensiveness. Human activities, such as irrigation and fertilization, are critical for wheat growth, but due to data limitations, these factors could not be fully incorporated into the model [39]. Future research could explore the inclusion of alternative data sources or develop relationships between available datasets to better represent these human-driven factors. By integrating these variables into the multimodal framework, more accurate and reliable prediction models could be developed. Deep learning is often regarded as a complex \"black box,\" where the training methods and adjustments to internal parameters introduce uncertainties in the model's outcomes [40]. While the use of explainability analysis tools, such as SHAP, has provided insight into the contribution of individual features, this study mainly focused on their global importance without examining their temporal dynamics in greater detail. In this study, predictive models integrate both spatial and temporal scales, increasing the complexity of the dataset and the diversity of the features involved [41]. While this integration enhances the potential for achieving high prediction accuracy, it also makes the model more challenging to interpret. Therefore, future studies should explore the temporal aspects of feature importance, focusing on how time-dependent factors influence crop carbon flux across different years and growth cycles. This would help improve model performance over varying time scales and lead to a better understanding of the temporal mechanisms driving carbon flux.
## 3 Sociocentric and Egocentric Centrality Measures
### Complexity Analysis of the Sociocentric and Egocentric Measures  
In this section, the complexity of the sociocentric and egocentric betweenness metrics is analysed. The main goal is to assess message overhead and time complexity.  
For the sociocentric betweenness measure, the nodes need to collect the global network topology information before performing the calculation. A straightforward way is as follows: (i) compute the length and the number of geodesic distances between all node pairs; (ii) for each node, calculate every pair-dependency, and sum them up. Consequently, this naive algorithm will consume \(\Theta(N^{3})\) time, where \(N\) is the number of nodes of the network. The well-known Brandes' algorithm can be efficiently calculated in \(O(NM)\) time [30], where \(N\) and \(M\) represent the number of nodes and edges of the network, respectively. The message overhead over the entire network generally needs \(O(N)\) message copies and \(O(D)\) time steps for each node's message, where \(D\) represents the network diameter [30].  
For the egocentric betweenness measure, the nodes require only local network topology information to carry out the calculation. The EBM calculation demands a computation complexity equal to \(O(k^{3})\) for a square matrix of \(k\times k\) dimensions, where \(k\) is the number of alters. The message overhead over the entire ego-network topology is \(O(k)\), since each node needs to send the identification of its neighbouring nodes. Table 2 depicts the complexity analysis of the sociocentric and egocentric measures.  
Since it is known that \(k\) is typically much smaller than \(N\) (\(k\ll N\)), therefore the local measure approach can bring computational benefits for calculation.
## 4 Conclusions  
This paper describes the investigation of the effects of different turbine blade parameters and fine inspection port positions on the pumping and detection performance of the helium mass spectrometer leak detector under inspection conditions. The optimal selection of blade inclination, blade number, blade height, and fine inspection port position under specific working conditions is discussed, which provides a theoretical basis for the development of helium mass spectrometer leak detection technology and the structural optimization of multiport hybrid molecular pumps. The main conclusions are as follows: (i) Decreasing the blade inclination, increasing the number of blades and increasing the height of the blades can improve the sensitivity of the helium mass spectrometer leak detector. (ii) When the inclination angle of the turbine blade is 25\({}^{\circ}\), the number of blades is between 25 and 30, and the blade height is 3 mm, the extraction performance is ensured and the detection is highly sensitive. (iii) When the working pressure of the inspected part \(p_{1}\) = 10 Pa, the fine inspection port must be behind the 8 th-stage blade row; when \(p_{1}\) = 0.1 Pa, it is only necessary for the opening position to be behind the third-stage blade row. (iv) When the leakage rate is less than 0.001 Pa\(\cdot\)m\({}^{3}\)/s, the selection of the opening position is no longer restricted by the working pressure of the mass spectrometer chamber. When the leakage rate is 0.1 Pa\(\cdot\)m\({}^{3}\)/s, the opening position must be behind the 8 th-stage blade row. The detection sensitivity of the fine inspection port continues to increase after the 5 th-blade row, which reflects the advantages of countercurrent detection. (v) Given the minimum detectable leakage rate \(Q_{2}{}^{\prime}\) = 5 \(\times\) 10\({}^{-12}\) Pa\(\cdot\)m\({}^{3}\)/s, when the fine inspection port is located between the 7 th- and 8 th-stage blade rows, the return flow of helium is much greater than that of air. (vi) When the fine inspection port is between the 7 th- and 8 th-stage blade rows, better leak detection performance, a larger pressure detection range, and the working pressure of the mass spectrometer chamber are ensured.  
Conceptualization, B.L. and X.W.; methodology, B.L. and D.B.; software, B.L. and D.H.; validation, B.L. and D.H.; formal analysis, B.L. and D.H.; investigation, B.L. and D.H.; resources, X.W.; data curation, B.L. and D.H.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and X.W.; visualization, B.L.; supervision, X.W.; project administration, X.W. and D.B.; funding acquisition, X.W. and D.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Special Project for the Development of Major Scientific Instruments and Equipment of China (Project No.: 2013 YQ130429). Not applicable. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Key Modules
### Channel Estimation and Compensation
#### 3.4.2 Minimum Mean Square Error Channel Equalization  
The minimum mean square error function can be expressed as:  
\[\overset{\wedge}{x}_{n}^{MMSE}=\text{argmin}\underset{x_{n}}{\overset{\wedge }{y}_{n}^{MMSE}}-C_{n}x_{n}\bigg{|}^{2} \tag{14}\]  
Derivation of Equation (14) and let the partial derivative be 0 and the equalization coefficient is shown as:  
\[C_{n}=H_{n}{}^{*}(\delta^{2}I+H_{n}^{2})^{-1} \tag{15}\]  
where, \(H_{n}\) is estimated by OMP that have been convert into frequency domain, \(\delta\) means the channel noise.
### Related Work  
In the health field, fine-grained user authentication is an important security measure that aims to ensure that only authorized individuals or entities can gain authentication from other specific entities, maintaining personal privacy and data security. In this section, we summarize existing fine-grained authentication schemes for healthcare systems.  
[PERSON] et al. pioneered an attribute-based fine-grained access control scheme to secure communication in the client-server architecture [5]. This groundbreaking scheme utilizes smart cards and biometric authentication for verification purposes. Furthermore, it enables the establishment of session keys to encrypt subsequent communications. However, it is important to highlight that the solution's communication overheads are relatively substantial, potentially impacting the overall user experience.  
[PERSON] et al. [6] introduced an access control with fog computing to achieve a more optimal balance between efficiency and security. This approach is well suited for a range of scenarios, including data storage, directory management, and file organization. However, [PERSON] et al. in [7] reported that the scheme proposed by [PERSON] et al. cannot achieve mutual authentication or resist device impersonation attacks.  
[PERSON] et al. recognized the sensitivity of medical data and consequently introduced a lightweight privacy-preserving authentication and fine-grained access control solution: PAASH [8]. They presented an elliptic curve cryptography (ECC)-based certifi-cateless signature scheme. Simultaneously, they employed attribute-based encryption and signature technology to achieve precise access control. Nevertheless, [PERSON] et al. [9] highlighted that the PAASH fails to counter impersonation, forgery, and modification attacks.  
There are also works for securing communication in WBANs. The authors of [PERSON] et al. in [10] offered a robust authentication and access control solution by using expensive  
Figure 1: WBANs network topology.  
bilinear pairing. However, the password of the user can be effectively guessed by the adversary if there is no use of \"modulus\" operations.  
Similarly, one research study [11] designed an E-health-oriented proposal that is relevant to authentication, key agreement, and access control. Furthermore, this study was the first to propose a method of transferring the ownership of patient information from the former physician to the new physician. Before the application of this charming proposal, forward secrecy and three-factor security should be applied.  
For the scenario of wireless medical sensor networks, [PERSON] et al. [12] proposed multiple solutions for user-server authentication, patient-server authentication, and user-patient authentication scenarios. However, the password verifier table is stored in the registration center and can face password-guessing attacks, which lead to the exposure of the user's password.
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 3 Real-Time Guided Counterfactual Explanations  
**Real-Time** **G**uided **C**ounterfactual **E**plansions **(RTGCEx)** is a model-agnostic algorithm that uses an autoencoder for generating real-time counterfactual explanations for a given black-box model \(f(\cdot)\).  
In the RTGCEx framework, as depicted in Figure 1, the generation of counterfactual explanations is achieved through the interaction of three components: a _Generator (G)_, an _Autoencoder (AE)_, and a _black-box model \(f(\cdot)\)_. The black-box model \(f(\cdot)\) can be any machine learning model, as long as the predict function is accessible. As discussed in Section 1, the counterfactual explanations produced by RTGCEx must satisfy certain criteria, such as data closeness, closeness to the data distribution, and user-specified explanations, among others. To ensure that these properties are upheld, given a trained black-box model \(f(\cdot)\), RTGCEx involves two phases of training:  
**Autoencoding phase:**  
Involves training an autoencoder by minimizing a loss function that measures the distance between the original samples \(\mathbf{x}\in\mathcal{D}\), where \(\mathcal{D}\) denotes the dataset on which \(f(\cdot)\) has been trained, and their reconstructions \(\mathbf{x}^{\prime}\).
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 1 Introduction  
The Internet of Things (IoT) represents a transformative networking architecture that holds the promise of enhancing our daily lives by imbuing every physical object in our environment with smart capabilities, enabling them to sense their surroundings and engage with other connected devices. Components such as software, wireless networking technologies, and sensors-actuators contribute to the intricate framework of IoT. The rapid proliferation of sensors, actuators, and smart devices connected to the Internet has fueled the momentum of IoT, allowing for data collection and remote control over physical entities. This integration of physical and computer-based systems has ushered in improvements in dependability, accuracy, and financial efficiencies [1]. However, this expansion has also introduced security risks, particularly concerning sensitive data. As the number of connected devices grows, security threats become increasingly hazardous, with large businesses being prime targets for cyberattacks aiming to compromise company reports, credit card data, and customer privacy documents. The long-term viability of IoT hinges on its security measures, as the constrained resource structure of IoT devices makes them vulnerable to the damaging consequences of attacks. Anticipated increases in attacks against IoT devices underscore the importance of securing the most crucial routing protocol for Internet Protocolversion 6 (IPv6)-based Low-Power Wireless Personal Area Networks: the Routing Protocol for Low-Power and Lossy Networks (RPL). Numerous cyberattacks have targeted a wide array of IoT applications and their integration services online. The primary focus of the most frequent RPL attacks is the resource utilization of nodes in the IoT ecosystem, leading to significant implications such as the loss of data packets, high computational loads, and consistency issues with node energy for Low-Power and Lossy Networks (LLNs). To ensure the resilience of IoT, proactive measures must be taken to mitigate the negative impacts of cyberattacks and fortify the security of this interconnected landscape [2].