esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Machine Learning Applications in Big Data
### Finance  
In the finance industry, ML is transforming how financial institutions manage risks, detect fraud, and optimize investment strategies. The ability to analyze large volumes of transactional data, market trends, and customer behavior allows ML models to identify patterns and anomalies that may indicate fraudulent activities. While traditional data mining algorithms are proficient in analyzing structured datasets to detect fraud throughrule-based pattern matching and historical data analysis, ML offers a transformative edge by handling both structured and unstructured data at scale. For instance, ML models can process transactional records alongside unstructured sources like customer complaints or social media signals to dynamically adapt to new fraud schemes [35; 36; 37].  
Unlike static rule-based data-mining systems, ML models can generalize across scenarios, enabling real-time detection of anomalies in high-frequency financial transactions. An example is the deployment of DL techniques in credit card fraud detection, where models analyze transaction attributes (e.g., time, location, and amount) alongside behavioral patterns, thereby reducing false positives and improving detection accuracy. Building on these capabilities, ML-based fraud detection systems excel at analyzing millions of transactions in real time, identifying suspicious activities with high precision while minimizing false positives. Through continuous learning from newly observed data, these systems evolve to detect emerging fraud patterns and adapt to the ever-changing landscape of financial crime [38; 39; 40; 41].  
Risk management is another critical application where ML models assess credit, market risk, and operational risk by analyzing diverse datasets, including historical market data, financial statements, and economic indicators. These models help financial institutions predict potential losses, optimize capital allocation, and comply with regulatory requirements. In the realm of algorithmic trading, ML algorithms process vast amounts of financial data to predict stock prices, execute trades, and manage portfolios, often at speeds and accuracies unattainable by human traders. These models can adapt to changing market conditions in real time, making them invaluable in high-frequency trading environments [42; 43; 44; 45].  
Moreover, ML is increasingly used in customer relationship management (CRM) within the finance sector. By analyzing customer data, ML models can predict customer needs, personalize financial products, and optimize marketing strategies. These applications enhance customer satisfaction and retention by providing tailored financial services and improving the overall customer experience [46; 47; 48; 49].
## 3 Purpose of the Two-Factor Authentication Method  
_The Threats That Two-Factor Authentication Addresses_  
The need for two-factor authentication has increased as institutions, governments, and individuals realize that passwords alone are not secure enough to protect user accounts in the current technical format. The average cost of data breaches increases yearly, with financial losses of billions of dollars annually. 2 FA protects against a multitude of threats. The most common threats include [26]:  
1. Stolen passwords--A traditional password can be used by anyone who takes possession of it. For example, if a user writes down their password on a paper medium, that password can be stolen to gain access to an account. After entering a password, 2 FA validates the user with a second device [27; 28].
2. Phishing attempts--Hackers often send emails that include links to malicious websites designed to infect a user's computer or persuade them to enter their passwords. Once obtained, a password can be used by anyone who manages the hacking attempt. 2 FA combats phishing by adding a second validation layer after entering the password.
3. Social engineering--Hackers often simply manipulate users to give up their passwords. By posing as an IT professional at the user's company, they can gain the user's trust before asking for login credentials. 2 FA protects against this by validating the location and IP of each login attempt after entering a password.
4. Brute-force attacks--In a brute-force attack, a hacker randomly generates passwords for a particular computer until it matches the correct sequence. The second level of protection of 2 FA requires a login attempt to be validated before granting access.  
* [5] Keylogging--If a user has not written down their password, hackers can use malware to track and copy a user's password as they write it using the computer keyboard. Hackers track every keystroke and store the password. The second level of validation in 2 FA allows a user to ensure that the login attempt is theirs, even if their password has been compromised.
## 2 Generic Pathways for Plant Response to Abiotic Stresses
### Reactive Oxygen Species (ROS)  
During abiotic stresses, such as salinity and drought, overproduction of ROS results in perturbation of the cellular redox state and thus poses the threat of developing the toxic event that is known as oxidative stress. However, ROS are usually present at a low level in many organelles, and they act as significant secondary messengers in the plant's response to abiotic stresses and secondary metabolism. They are considered very effective secondary messengers due to their spatiotemporal flexibility [31].  
There is a synergistic effect in the plant's defense system between Ca2+ and ROS signaling [42; 43]. The respiratory burst oxidase homolog (_RBOH_) gene family encodes for an enzyme known as the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. This product enzyme is considered the major contributor of de novo synthesized ROS extracellularly. The _RBOH_ gene is regulated and activated by the previously mentioned calcium-dependent protein kinase (CDPK), which facilitates its phosphorylation. Thus, in order to produce more ROS, Ca2+ levels ought to be increased. Vice versa, elevated levels of ROS lead to the increased concentration of Ca2+ ions by the interaction of the H2O2 and hydroxyl radicals with the Ca2+-permeable cation channels such as the voltage-independent Ca2+-permeable cation channels (VICCs) and the hyperpolarization-activated Ca2+-permeable cation channels (HACCs). This results in a noticeable transient increase inthe cytosolic Ca\({}^{2+}\), which is involved in several fundamental physiological and regulatory activities [26; 44; 45].  
Similar to Ca\({}^{2+}\), ROS interact with other signaling molecules such as the MAPKs. The mitogen-activated protein kinase is one of many important signaling molecules that initiate cascades of events that are involved with transducing stress-related stimuli such as enhanced levels of ROS or calcium signaling. It was discovered that ROS upregulate the activity of the _ANP1_ kinase in _Arabidopsis_[46]. and its functional homolog _NPK1_ in tobacco [43], which trigger the downstream MAPK cascades. Several molecular genetic experiments indicated that _ANP1_ is responsible for the regulation and activation of _AtMPK3_ and _AtMPK6_[39]. The protein kinase products of MPK3/MPK6 are responsible for the direct phosphorylation of the transcription factors associated with the plant's defense system, such as WRKY and ERF. Many other studies have proved the association between ROS and MAPKs in several plant models, indicating their function in the plant's response to abiotic stresses [47; 48].
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.7 Remote Incremental Linking (RIL)  
This modular updating system, designed for the mica2 mote, support static and dynamic updates [9]. A program is built from a number of functions, reserving space at the end of each function to allow for some future expansion (slop space). These functions are linked remotely from the target nodes, at a more capable node such as the base station. The new version of a function can be written in program memory at the same start address, as long as it fits, without the need to update references to this function elsewhere in the program. Otherwise, either adjacent functions or the new function are moved: whichever minimizes page writes. The new version (code and/or data) is delivered in an incremental manner, using diff-scripts generated by the Xdelta algorithm. Single pages can be updated, reducing the need for RAM use, and allowing the script for the next page to be downloaded while the previous page is being written (using ATMega128's read-while-write). The boot-loader interprets the scripts and updates the program memory pages. The system is probably more suitable for minor, incremental upgrades, and the need to retain the capability for full system upgrades is identified by the authors.  
The system has no specific features relevant to autonomous updates, but the additional granularity associated with the inherent advantages of size reduction might be of value.
## 4 Results
### Scenario A
The power turns out to be less than 500 mW in all cases, which is certainly within the reach of state-of-the-art optical transceivers [39], by combining, for example, high DFB and optical amplification [40]. Figure 8: Minimum distance between satellites in the constellation relative to changes in the \(F\) value for Scenario A. We chose an \(F\) value of 13 to maximize the safe distance between satellites. Figure 9: Path loss variations for links established between satellite \((0,0)\) and its adjacent neighbors in the (**left**) upper orbit and (**right**) lower orbit, observed over one orbital period. Another practical conclusion that can be drawn from these figures is the fact that, especially for neighbors in adjacent satellite orbits, the required power varies significantly over time, and hence, it may be advantageous to consider adaptive schemes to calibrate the transmission power to its required value rather than simply transmitting the maximum value obtained in the figures. Figure 12 shows how the required power scales with the specified data rate \(R_{\rm b}\). As expected, the required optical power increases as the data rate rises. For 100 Gbps, it surpasses 1 W [41]. Note that even at such data rates, power requirements levels remain low enough to be achieved in miniaturized terminals using optically amplified transceivers [42].
## 4 Conclusions  
Hydrogen permeation behavior and the mechanism of AISI 4135 steel in different marine corrosion zones were investigated using an in situ hydrogen permeation monitoring system via outdoor and indoor hydrogen permeation tests. The conclusions can be summarized as follows:  
1. A good performance of the in situ hydrogen permeation monitoring system was present in in both the outdoor and indoor hydrogen permeation tests. Hydrogen permeation into steel during corrosion in different marine corrosion zones can be evaluated with good accuracy through the collected hydrogen permeation current data during outdoor testing.
2. The 3-month outdoor hydrogen permeation tests showed that the diffusible hydrogen content of steels in the marine atmospheric, splash, tidal and immersion zones were 3.15 \(\times\) 10\({}^{-3}\), 7.00 \(\times\) 10\({}^{-2}\), 2.06 \(\times\) 10\({}^{-2}\) and 3.33 \(\times\) 10\({}^{-2}\) wt ppm, respectively.
3. It was found that the hydrogen permeation current density is positively correlated with the corrosion rate of the steel in the marine environment. A large hydrogen permeation current of the steel exposed to the immersion zone was observed during the outdoor test, which is considered to be related to the formation of FeS in the corrosion products caused by SRB.
4. There are no obvious changes in the OCP of the steel during tidal cycles, and the change in the hydrogen permeation current is mainly controlled by the pH of electrolyte and oxygen concentration beneath the rust layer.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12062785/s1](https://www.mdpi.com/article/10.3390/app12062785/s1)]([https://www.mdpi.com/article/10.3390/app12062785/s1](https://www.mdpi.com/article/10.3390/app12062785/s1)), Figure S1: Details of dimensional information for metal specimens; Figure S2: Macro-morphologies on the cross-section of the sensors after exposure in the atmospheric zone (a), the splash zone (b), the tidal zone (c), and the immersion zone (d), respectively.  
Data curation, Y.X.; Formal analysis, D.L.; Funding acquisition, Y.H.; Methodology, L.Y.; Project administration, Y.H.; Software, F.C.; Validation, Z.W.; Writing--original draft, Y.X.; Writing--review & editing, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China under Grant <No. 41976033>.  
The raw data related to this manuscript will be made available on request.  
The authors declare no conflict of interest.
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 1 Introduction
Indeed, in [13], first, multiple FCDNN-layers are used for frequency-aware pilot design, and then a hybrid neural network is placed in the UE to acquire the channel knowledge. The latter is composed of a FCDNN-layer per subcarrier, three CNN-layers, and a Non-Local Attention module. Instead [14], proposes an encoder (FCDNN, normalization layers, and an attention algorithm) and a convolutional architecture decoder composed of seven layers. Despite the advantages of an accurate estimated channel using neural networks, all of the above cited works require different stages to acquire the full channel matrix (e.g., two neural networks or one neural network and an interpolation along subcarriers). A different solution is considered in [15] using one CNN, called fast super-resolution (FSRCNN). The latter uses eight CNN-layers to produce the channel matrix from the pilot information. However, the pilot information is dimensioned though zero-padding in the input, and each layer uses multiple channels. These two factors lead to an unnecessary growth of the complexity and possible overfitting. All of the above considerations result in a critical computational cost when implemented in hardware. To address this concern, we propose a low-complexity CNN which treats the channel information in the Demodulation Reference Signal (DMRS) as a low-resolution noisy 2D image. The extrapolation of the full channel matrix is performed by gradually adjusting the padding at each layer. Because the channel is described by complex values, this aspect is handled by applying the neural network in the real and imaginary parts, separately. The contributions of this paper are summarized as follows:  
1. The CNN is used for increasing the resolution of the 2D channel image. Only one CNN is implemented to generate the full channel grid from the DMRS. 2. The computational cost is significantly reduced compared to other channel estimation techniques. 3. The improvements of the proposed approach are demonstrated by virtue of analyzing several DL-channel estimation techniques in terms of complexity and MSE in a 5G NR system. The stage-by-stage complexity analysis offers a tool for assessing the complexity of any neural network incorporating LSTM, FCDNN, or CNN layers. This paper is organized as follows. In Section 2, the 5G NR system model is described, and the challenges of channel estimation are discussed. In Section 3, the traditional channel estimations are introduced while highlighting advantages and drawbacks. In Section 4, the focus is on the DL-channel estimations with particular attention to the proposed low complexity CNN. In order to evaluate all of the different channel estimation methods, the computational costs are compared in Section 5, and MSE analysis in a 5G NR simulation is presented in Section 6. Finally, the conclusions are drawn in Section 7.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Node-Level-Focused Energy Conservation Schemes
#### 4.1.3 Mobility-Based Techniques  
The mobility-based schemes use a few mobile nodes to achieve energy conservation in the network [62]. They leverage the mobility of nodes to dynamically adapt to changing environmental conditions and network requirements, thereby contributing to a prolonged WSN's operational lifespan. By moving the sink or relay closer to the sensor nodes, they achieve energy savings thanks to reduced link errors, contention overhead, message loss, and forwarding range during communication. This eliminates multi-hop communications and reduces the energy consumed by the radio of sensor nodes. Mobility-based energy saving is achieved via a mobile sink and/or mobile relay [65]. Integrating mobile sinks or relays into WSNs introduces dynamic data collection mechanisms. Mobile sinks, equipped with enhanced processing capabilities, can intelligently navigate through the sensor field to collect data directly from nodes. By minimizing the need for extensive data transmissions and long communication routes, this mobility-based approach significantly reduces the energy expenditure of individual sensor nodes.  
The impact of mobility-based schemes is highly effective in LWSNs compared to traditional WSNs because mobility-based schemes greatly reduce the number of multi-hop transmissions, which is the main source of energy wastage in LWSNs.
# Review of Smart-Home Security Using the Internet of Things
## 3 Smart-Home Ecosystem
### Physical Home Security and Monitoring
One of the primary concerns with smart-home surveillance cameras is their susceptibility to hacking. Intruders can gain remote access to these cameras via unsecured networks or through the exploitation of firmware vulnerabilities [9; 88]. Once accessed, these cameras can be manipulated and turned off, alter their recordings, or even use them to secretly monitor the activities of the household. Moreover, these cameras collect vast amount of data, presenting significant privacy concerns [89; 90]. If intercepted, video footage can be used maliciously, revealing the personal routines and private moments of individuals. The encryption of data streams and the secure storage of recorded footage are, thus, critical components that need reinforcing across smart-home security systems. Furthermore, as outlined in the research, advanced methods, like ML algorithms, are increasingly used to differentiate between regular activities and potential security threats [91]. These systems can identify an intruder even under challenging conditions, such as in cases with low light or when faces are partially obscured. However, these sophisticated features also introduce complexity into the system, potentially leading to false positives or the mishandling of data. To address these challenges, enhancing the physical security of the camera itself [88], such as through tamper-detection mechanisms, and improving the encryption used during data transmission are vital. Also, manufacturers need to ensure regular firmware updates to protect against the latest threats and implement robust authentication measures to prevent unauthorized access. Future research should focus on developing more resilient ML models that can accurately detect anomalies while preserving privacy. There is also a growing need for comprehensive frameworks that govern data handling processes, ensuring that user data are protected and only accessible under strict conditions. This holistic approach to security will be essential as smart-home technologies become increasingly integrated and complex.
## 1 Introduction
[44] designed an improved discrete binary particle swarm optimization via jointly considering achievable rate and load balance. [PERSON] et al. [45] used a competition-mechanism-based multi-objective PSO algorithm for satellite systems. In this paper, we present a comprehensive algorithm for task offloading with data-dependent constraints in SEC. Firstly, we formulated a MILP model and proposed a Petri-net-based amending method to fulfill dependency constraints. Secondly, we introduced a multi-objective wolf pack search algorithm, which balances convergence and diversity, minimizes computational overhead with adaptive mechanisms, and uses a Lamarckian-learning-based multi-neighborhood search to break local optima. Finally, we conducted extensive numerical experiments to evaluate the algorithm's performance. The main contributions of this paper are summarized as follows:  
* A MILP model is proposed for task offloading with data-dependent constraints in an SEC network. In addition, we consider a time-varying satellite network associated with orbit elements. * We construct a Petri-net-based amender from a given candidate solution. This amender effectively describes the coupling between tasks with data dependencies and edge servers. Furthermore, we introduce a Petri-net-based constraint amending method with polynomial time complexity, ensuring that offloading results conform to the constraints. * A strengthened dominance relation sort is established to balance the convergence and diversity of nondominated solutions. It does not require a significant increase in computational cost, ensuring that the obtained non-dominated solution set is both close to the actual Pareto front and not overly concentrated. * An MOWPS algorithm is presented that incorporates adaptive mechanisms to reduce computational overhead and uses Lamarckian-learning-based multi-neighborhood search to avoid local optima. Our experiments demonstrate that MOWPS outperforms existing algorithms in all testing instances. * The remainder of this paper is organized as follows. Section 2 outlines the MILP model, while Section 3 presents the proposed MOWPS algorithm, including the Petri-net-based amending method, the strengthened dominance relation sort, the adaptive evolution mechanism, and the Lamarckian-learning-based multi-neighborhood search. Section 4 presents the numerical experiments and their results. Section 5 offers a discussion of the findings, and finally, Section 6 concludes this paper.
## 4 Results and Discussion  
Table 2 lists information on the C-SAR satellite data used in the experiment. Specifically, C-SAR/01 comprises the QPSI and PSII modes, C-SAR/02 includes the QPSI, QPSII, and FSI modes, and GF-3 incorporates the FSII and UFS modes. The experimental data were classified and analyzed according to different satellites and imaging modes. In order to provide a more intuitive comparison of positioning accuracy for different imaging modes of different satellites, we conducted classification mapping and discussions. Some of the data points are not visible in the image, either because certain equipment was not adjusted on the day of the satellite transit or due to the image's coverage area.
# Application of Machine Learning Techniques to
## 3 Methodology  
Machine learning algorithms are becoming more popular owing to their ability to identify the patterns and variances in large datasets composed of multivariate atmospheric covariates, location parameters, meteo-climatic information, tele-connection indices and attributes that influence precipitation [2; 49; 50] or the precipitation and temperature statistics [51; 52]. In this study, we used the Python module scikit-learn [53], which integrates a broad range of machine learning algorithms for supervised as well as unsupervised learning. As aforementioned, this framework consists of five steps: delineation of homogeneous climate zones; selection of GCMs; validation of selected GCMs; selection of forcing Scenarios to be used for the predictions of the climate change; and climate change projections and trend detection. Figure 3 presents the methodology flow diagram adopted in this study. Further details of each step are discussed in the subsequent sections.