esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 2 IoT and Wind Energy
### Composition of WECS
The bit block that corresponds to the \(j\)th component is represented by \(b_{ij}(t)\), where \(b_{ij}(t)\in\left\{0,1\right\}^{k}\). Next, a repeat-accumulate code is used on \(b_{ij}(t)\) to produce a code word, where \(c\) refers to the encoded bits or codewords. The codewords are organized in a sequential manner to create \(m_{i}(t)=\left[c_{i1}(t),c_{i2}(t),\ldots,c_{ip}(t)\right]\). Once the \(m_{i}(t)\) is modulated onto the wireless carrier signal, the resulting carrier signal \(s_{i}(t)\) is transmitted from the wind turbine to the base station:  
\[y(t)=h\cdot s_{i}(t)+n_{w} \tag{2}\]  
where \(n_{w}\) represents the additive white gaussian noise (AWGN) with a mean of zero and a standard deviation of \(\sigma_{w}\) and \(h\) is the fading component. After receiving \(y(t)\), the receiver carries out the inverse procedure (such as demodulation, decoding, demapping, etc.) to create the observed state. Figure 5 depicts the communication architecture for wind turbines based on IoT sensors. SCADA and CMS: Currently, SCADA systems offer advanced services and capabilities for WECS that go beyond basic monitoring and management of wind turbines. CMSs are essential systems that are seamlessly connected with SCADA systems. CMSs utilize a variety of methods to detect defects in wind turbines at an early stage. The incorporation of CMS systems has shown significant improvements in the functioning and upkeep of wind turbines. CMS systems commonly employ a greater number of sensors with higher sample frequencies, as opposed to SCADA systems [14]. CMSs provide significant advantagesin data communication, calculation, and storage, in addition to increasing overall costs. Consequently, numerous proposals are put forward to utilize SCADA data for condition monitoring in order to decrease the expenses of wind energy conversion systems [16]. Nevertheless, a thorough examination of specialized CMS in comparison to SCADA-based configuration management (CM) reveals that CMSs are considerably more expensive but possess more diagnostic capabilities due to their higher frequency of information.
## 3 Fault Diagnosis: Machine Learning-based Adaptive Neuro High-Order Variable Structure Observer  
Based on Figure 1, the SVM-based, adaptive, neuro variable structure observer fault diagnosis and fault-tolerant control for a robot manipulator are presented in this research. Therefore, the main objectives of this research are fault diagnosis and fault-tolerant control. The fault diagnosis of the robot manipulator based on the proposed algorithm has two main steps. To increase the fault detection and classification, the signal estimation accuracy of the normal and abnormal signals based on the neural adaptive variable structure observer is presented in the first step. The VSO is a robust observer that can be used for signal estimation. A high-order super twisting technique is used to mitigate the issue of chattering in VSO. In addition to effectively estimate the signal, accuracy is the other challenge that needs to be addressed. To increase the signal estimation accuracy, a neural-network technique is introduced. Moreover, the adaptive technique is used to improve the reliability and robustness. The second step for fault diagnosis is fault detection and classification. To increase the fault detection and identification accuracy, SVM is considered in this research.  
A variable structure controller (VSC) is a robust and reliable control algorithm, and is one of the most suitable candidates for the fault-tolerant control of robot manipulators. This technique has three basic problems: (a) the chattering phenomenon; (b) the system's dynamic dependency; and (c) robustness in faulty conditions. A backstepping control algorithm is used in the work to address the chattering issue. Next, the difficulty of determining a system's dynamics under uncertain conditions is evaluated using a fuzzy algorithm. Moreover, the robustness and reliability of the fault-tolerant controller is improved using an adaptive procedure.
## 2 Data and Methods
### Data
#### 2.1.3 Meteorological Data  
In addition to the remotely sensed data characterizing the vegetation cover, the performance of NEE is influenced by weather parameters such as temperature, dew-point temperature, solar radiation, and precipitation. The global dataset AgERA5 was chosen as the source of this data [39].  
This service is based initially on hourly European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ECMWF ERA5) data at the surface level, which are available at 30-km spatial resolution (about 0.28125\({}^{\circ}\)). Data were processed to daily temporal resolution by summing or averaging depending on a particular parameter and corrected to a more refined surface description at a 0.1\({}^{\circ}\) spatial resolution. Aggregated data follow a local time zone definition and include several major agronomic parameters. The correction to the 0.1\({}^{\circ}\) grid was performed by applying grid and variable-specific regression equations to an ERA5 dataset interpolated at a 0.1\({}^{\circ}\) grid. The equations were trained on operational ECMWF's high-resolution atmospheric model (HRES) data at a 0.1\({}^{\circ}\) resolution.  
The following set of parameters was chosen for our purposes: 24 h mean of 2 m air temperature (_TemperatureAir_), 24 h mean dew-point temperature (_DevPoint_), precipitation flux (sum of total precipitation per day, _PrecipitationFlux_), and surface solar radiation downwards (sum of solar radiation per day, _Solar_). A description of the additional meteorological features generated and used in this work is provided in Section 2.2.2.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## Appendix A Derivation of \(\omega^{*}\) and \(\gamma^{*}\)  
As we know, the choice of these parameters is critical for the performance of the OAOR method, particularly in the context of signal detection in massive MIMO systems, so the objective is to minimize the mean squared error between the original signal \(\mathbf{x}\) and the estimated signal \(\hat{\mathbf{x}}(\omega,\gamma)\), which was already stated as  
\[\begin{array}{ll}\text{minimize}&f(\omega,\gamma)=\mathbb{E}[\|\mathbf{x}- \hat{\mathbf{x}}(\omega,\gamma)\|^{2}]\\ \text{subject to}&0<\omega<2,\quad 0<\gamma<1\\ &\rho(\mathbf{I}-\omega\mathbf{D}^{-1}\mathbf{G}^{H}\mathbf{G}+\omega\gamma \mathbf{D}^{-1}(\mathbf{D}-\mathbf{L}-\mathbf{U}))<1\end{array} \tag{1}\]  
Here, \(\mathbf{x}\) is the original signal vector, \(\hat{\mathbf{x}}(\omega,\gamma)\) is the estimated signal after applying the OAOR method with parameters \(\omega\) and \(\gamma\), and \(\rho(\cdot)\) denotes the spectral radius of the matrix. The objective function \(f(\omega,\gamma)\) is the expected mean squared error (MSE) between the original and estimated signals.  
To solve this optimization problem, we use the novel PSO algorithm. Let us derive the updated equations.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 3 System Model
### Design of Zone-Chain  
#### 3.2.1 Block Design  
Blockchain forms a tamper-resistant chain in the form of blocks. Given that EC involves extensive storage and updates of indexes and models, the block structure of Zone-Chain is specifically designed as shown in Figure 2.  
* Block Header: Essential for blockchain's security, it includes critical block information and a unique hash value identifying the content of last block. Any modification in previous blocks will alter subsequent hash values.
* Edge View: ENs may move to different coverage of ZSs in different rounds. Before each round, ZS \(m\) logs ID\({}_{n}\) of ENs within its service coverage into \(View_{m,i}[1,N|]\), marking absent ENs with 0. Zone-Chain can obtain a new global view \(View_{i}[|M_{i}|,N|]\) through consensus.
* Index Value: It measures the real-time node quality, communication resources, and security credibility of ENs.
* Selected Node Set: A set of ID\({}_{n}\) selected by the node selection strategy of Zone-Chain.
* Local and Global Models: For recording updated model parameters of local and global training.
* Local and Public Key: Used to record the public and local keys utilized in encryption algorithms for encrypting model parameters during data transmission between ZSs and ENs.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.5 Jacobian-Based Saliency Map Attack  
The Jacobian-based Saliency Map Attack (JSMA) is a saliency-based white-box adversarial attack method. It was proposed by [PERSON] et al. [52] to generate AEs capable of deceiving the Deep Neural Networks (DNNs) by using the Jacobian matrix to identify the most influential input characteristics that lead to a substantial change in the DNNs output.  
Unlike FGSM, JSMA aims to reduce the perturbations by controlling the number of features to be perturbated instead of the magnitude or quality of the perturbation. The goal then is to manipulate only a small number of pixels within the image, rather than disturbing the entire image, and monitoring the effects on the output classification. The observation is conducted through the computation of a saliency map using the gradient output of the network layer. Once the saliency map is calculated, the algorithm systematically identifies the pixel within an image that would have the most significant impact on fooling the neural network and proceeds to modify it. This iterative process continues until either the adversarial image has reached the maximum permissible number of altered pixels, or the intended deception is successfully achieved.  
For an original input data (i.e., image) \(X\), which is classified as label I, i.e., \(f(X)=l\). The attacker's goal is to add a tiny perturbation \(\delta_{x}\) to produce an adversarial sample \(X^{\prime}\) where \(f(X^{\prime})=f(X+\delta_{x})=l\). This can be summarized by following expressing (5).  
\[Arg\ min_{\delta_{x}}\|\delta_{x}\|\qquad s.t.\ \ f(X^{\prime})=f(X+\delta_{x})=l^{\prime} \tag{5}\]  
calculating the positive derivative for a given input sample \(X\), the Jacobian matrix is computed as expressed by the following Formula (6):  
\[J_{f}(X)=\frac{\partial f(X)}{\partial X}=\left[\frac{\delta f_{j}(X)}{\delta x _{i}}\right]_{i\in 1\ldots M;\ j\in 1\ldots N} \tag{6}\]  
When compared to FGSM, this technique demands more computational power due to the computation of saliency values. Nonetheless, it significantly limits the number of perturbed features, resulting in the generation of AEs that appear to be more similar to the original sample.
## 4 Conclusions  
In this study, mixed convection heat transfer has been numerically studied in a channel filled with a porous medium with potentially negligible upper and bottom walls thickness. The numerical results are presented for the Rayleigh number range of \(Ra=100\)-\(1000\) for different Peclet numbers (\(Pe=0.5\)-\(1\)-\(5\)). In view of the results presented, the main findings can be summarized as follows:  
* For all Rayleigh numbers, a center of rotation forms around the heater within the volume.
* Heat transfer increases as the Rayleigh numbers increase.
* When the Peclet numbers are small, the free convection mode is dominant. When the Peclet numbers are large, the forced convection mode is dominant.
* The results from the governing equations show the oscillation of the average Nusselt number along the heated element.
* When the effects of Nusselt number for different heater positions among different Rayleigh numbers are examined, the local Nusselt number increases as the Rayleigh number increases for more energy inputs into the system. The Nusselt numbers are very close to each other when the Rayleigh numbers are low, and where conductive heat transfer is dominant.  
Figure 8: Variation of average Nusselt number with Rayleigh number (\(c=0.33\)).  
Figure 9: Variation of average Nusselt number with Rayleigh number (\(c=0.66\)).  
* It is seen that heat transfer decreases when the Peclet number increases. However, when the Peclet number is low, heat transmission is more dominant than the heat convection in the flow medium.
* The results showed that increasing of the Rayleigh number enhanced the heat transfer for the same effective parameters.  
Conceptualization, F.O.; methodology, F.O.; investigation, F.O. and Y.V.; writing--original draft preparation, Y.V.; writing--review and editing, Y.V.  
This research received no external funding.  
The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Endocrine-Disrupting Compounds EDCs
### Atrazine Degradation  
Atrazine is an example of a pesticide compound whose residues in the environment cause changes in the endocrine system of living organisms [71; 72; 73]. Atrazine belongs to the s-triazine derivatives. Compounds from this group have a ring structure and are characterized by strong aromaticity and high chemical stability in biodegradation-based decomposition processes. Although the use of atrazine has been banned, it can still be detected in all types of natural waters due to its previous large-scale use.  
It was found that \(\gamma\) irradiation in the presence of humic substances (HS) (200 mg dm\({}^{-3}\)) combined with sodium bicarbonate (1 g dm\({}^{-3}\)) allowed for removing atrazine and its degradation products from contaminated water at a level \(\leq\) 20 times the maximum allowable concentration of atrazine and its metabolites (IMAC) in drinking water [74]. The reaction of e\({}_{\text{aq}}\) - (k = 4.8 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)) with atrazine is faster than the reaction of \({}^{\bullet}\)OH radicals (3.0 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)) with atrazine (about 50% higher), and this supports the assumption that reduction reactions dominate the radiolysis of atrazine, leading to a higher degradation yield. Reducing conditions can be achieved by the addition of a HS/carbonate mixture, which acts as a scavenger of hydroxyl free radical \({}^{\bullet}\)OH. In addition, the formation of active radicals from the oxidation of humic acid may also play an important role in atrazine degradation. As is shown in Figure 5, the dominant atrazine metabolic reactions are declhorination, dealkylation, and deamination. The synergistic effect of humic acid was also confirmed by Khan et al. [75]. As a result of the reaction with e\({}_{\text{aq}}\)\({}^{-}\) and the \({}^{\bullet}\)OH radical, a high degree of organic chlorine and nitrogen mineralization to chloride, nitrate, and ammonium ions can be achieved. The formation of acetate ions from carbon atoms of the side chains was also observed. It was found that the electron beam can be extremely effective in removing atrazine; 90% efficiency for the initial concentration of 6 mg dm\({}^{-3}\) was already obtained at the dose of 0.77 kGy [76].
## 5 Conclusions  
This study introduces an OOCA method designed to enhance the detection of faults and fractures in structurally complex subsurface environments. By integrating geological guidance with multi-scale frequency analysis, the method demonstrates clear advantages in both imaging quality and engineering relevance. The results from physical modeling and field seismic applications validate its robustness and applicability in real-world exploration settings.  
1. The proposed approach is fundamentally guided by structural geology. It employs local dip estimation to construct structure-oriented paired model traces and incorporates a distance-weighted scheme to improve spatial response. This configuration enhances sensitivity to reflector geometry variations and lateral discontinuities, enabling more precise delineation of fault boundaries and structural edges.
2. A Gabor-based multi-frequency fusion strategy is employed to capture both low-frequency continuity and high-frequency structural detail. This fusion enables coherent imaging of large-scale fault zones while simultaneously highlighting subtle secondary fractures, thus achieving enhanced resolution across multiple spatial scales.
3. Comprehensive validation using both physical models and field data demonstrates that the OOCA method outperforms conventional coherence attributes--including amplitude, curvature, and traditional C3 coherence--in terms of structural clarity, resolution, and noise resistance. Notably, in areas with documented lost circulation events, the method exhibits strong alignment with engineering observations,significantly improving the reliability of pre-drilling structural interpretation and risk prediction.  
In summary, the OOCA method offers a geologically consistent, directionally adaptive, and computationally efficient solution for high-resolution fault and fracture characterization. Its dual value in geophysical interpretation and drilling risk assessment highlights its potential for broad application in complex carbonate reservoirs.  
S.C.: Writing--original draft; S.L.: Methodology; Q.M.: Data curation; S.Y.: Supervision; L.Q.: Validation. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the R&D Department of China National Petroleum Corporation (2022 DQ0604-01), Prospective, Basic, and Strategic Technology Research Project of PetroChina (2021 DJ0606), and Gansu Science and Technology Program (23 ZDGA004), and the CNPC Digital and Intelligent China Major Project \"Petroleum and Petrochemical Industry Software\"--Drilling Safety Control Software (2024 ZZ46-05).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
All the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. The funder was not involved in the study design, collection, analysis, interpretation of data, writing of this article, or decision to submit it for publication.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion  
### Physicochemical Characterization of Zeolite, Biochar, and PZC 7:3  
#### 3.1.1 Basic Physicochemical Properties  
Table 1 displays the physicochemical characteristics of zeolite, biochar, and PZC 7:3, such as the pH value, electrical conductivity (EC), and hydrogen/carbon ratio (H/C). The pH levels of zeolite, biochar, and PZC 7:3 were all alkaline. As in previous reports, oxygen-containing functional groups generated during pyrolysis resulted from an increased pH [40; 41]. In addition, it was shown that alkaline ions such as Na\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), and Ca\({}^{2+}\) remaining in the biochar ash led to a high pH of biochar [42]. PZC 7:3 was a combined composite of biochar and zeolite. Its pH was increased compared to zeolite due to the influence of biomass carbonization, which is similar to the research findings of [PERSON] [43]. Generally, H/C can reflect the aromaticity of biochar, and a smaller H/C indicates higher aromaticity [44]. The H/C ratio of the PZC 7:3 was lower than that of biochar, demonstrating that the addition of minerals enhanced the aromaticity of biochar and increased the stability of PZC 7:3, which is similar to the findings of [PERSON] [45] and [PERSON] [46].  
The SSA of an adsorbent is a parameter that characterizes its adsorption capacity [29]. Table 2 displays the pore structure parameters of the materials. The specific surface area of zeolite after pyrolysis was not significantly different (\(p>0.05\)) compared to that of PZ. Still, the pore volume and pore size were significantly increased (\(p<0.05\)), which may be due to the evaporation of water molecules in the pores of zeolite by heating. The specific surface area of PZC 7:3 was not significantly different (\(p>0.05\)) compared to that of the biochar. The reason may be that biochar was attached to the surface of zeolite during the pyrolysis of the composite [47], so the specific surface area of PZC 7:3 was closer to that of the biochar.
## 4 Hybrid Harmony Search-Simulated Annealing Algorithm
### Step 3: Improvisation  
In the proposed HS, to increase the speed of convergence, multi solutions are generated at each iteration and called HMnew. Each newly generated solution is taken from the HM randomly with the probability of HMCR, otherwise, it will be generated randomly within the range of the harmony vector. To further enhance the intensification of the method, the proposed HS implements the local optimization in three different ways: within a depot, between two depots, and within the vehicle route. Hence, five multi-neighbourhood local search techniques are proposed:  
1. **Swap:** exchange the position of two random customers within the same route or between two different routes. Swapping can be done within a depot or between the depots.
2. **Insertion:** insert a customer in between two other random customers within the same depot.
3. **Relocation:** relocate the customers from the current depot to a new depot.
4. **2-Opt:** swapping two customers in the same vehicle route and reverse the substring between the swapped customers.
5. **3-Opt:** deleting three edges of the customers in the same vehicle route and create another three sub tours in three possible ways: non-reversing substring, with one reversing substring, or with two reversing substring.  
The local optimization is applied to the new solution with the probability of \(PAR_{it}\) and the choice of multi-neighbourhood local search technique is randomly selected with equal probability. As mentioned earlier, we extended the methodology in [6] by adding two popular local search methods: 2-opt and 3-opt, as the local optimization techniques. The examples of these five techniques are graphically shown in Figure 2a-f.  
Figure 2: The figures show how each of the multi-neighbourhood search techniques finds their neighborhood: (**a**) Swap within depot: customer 1 and 2 are swapped within the same depot; (**b**) Insertion: customer 3 are inserted in between depot and customer 1 in the route sequence; (**c**) Swap between depots: customer 6 in depot 1 is swapped with customer 1 in depot 2; (**d**) Relocation: customer 6 in depot 2 is moved to depot 1; (**e**) 2-Opt: swap customer 1 and 4 in the same route and the route sequence between swapped customers are reversed; (**f**) 3-Opt: three possibilities of 3-Opt which are either non-reversing, one reversing or two reversing substring.  
_Step 4: Update HM_  
To update the solutions in HM, the HM\({}_{\text{new}}\) created at each iteration is combined with current HM. The solutions are sorted according to the value of the objective function. The best solutions with the size of HMS will be kept for the next iteration.  
_Step 5: Termination_  
Repeat _Step 3_ and \(4\). The proposed algorithm will be terminated when either the _MaxIt_ is reached or \(t\) consecutive non-improving iterations are performed.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
EMR and Data Storage: these are electronic medical records (EMR) of patients stored in the telemedicine system; they include the patient's medical history, diagnosis, treatment plan, and other relevant information. The data storage component ensures that the data are stored securely and can be accessed by authorized personnel.