esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The use of a simple finite element beam solution is the main difference with the local material basis method proposed [13; 14] based on a localized linearization approach. Our proposal can be easily implemented in all well-established commercial finite element codes for structural application to achieve a robust and optimized solution. The tool is firstly validated theoretically with benchmarks on beam-like structures undergoing large deflections. Then, experimental results are produced on a monitored pipe buried in a wedge of land subject to an artificial slide. Negligible displacements are assumed far from the landslide influence as boundary conditions of the model. Finally, the displacements predicted by the sensor-modeling proposal are compared with the displacement trend measured in situ. A web application is also implemented for data acquisition and processing, to provide a real-time alarm detection system. The novel idea of the proposal can be summarized as follows: how to use FBG sensors for monitoring large displacements of pipelines using a simple and robust finite element model. In addition, experimental validation is provided. The paper is organized using the following structure: Section 2 recalls the FBG technology for the strain measurement; Section 3 introduces the geometrically exact 3D beam model for reconstructing the finite kinematics of the pipeline due to discrete measured strain values; Section 4 describes the finite element model, the efficient solution of the nonlinear discrete equations, and the validation of the numerical model with benchmark structures; Section 5 concerns the experimental setup for a monitored pipe buried in a wedge of land subject to an artificial slide; Section 6 reports and compares experimental and numerical results, in order to assess the proposed monitoring tool; Section 7 shows some details of the web application; conclusions are drawn in the last section.
## 4 Results and Discussion  
### Modeling Example  
As an example, we consider a DHS with a maximum thermal load of consumers of 18 MW, an electrical load of 8 MW, and a heating period of 6000 h. Based on the optimization performed using a genetic algorithm in the MathLAB/Simulink 10.3 (Load Frequency Control) software, a graphical model of thermal energy source operation was obtained (Figure 9) [101; 102; 103].  
The mix of the equipment selected for the considered DHS, the installed capacity of each type of thermal energy source and the volumes of consumed energy resources are presented in Table 2.  
Based on the calculation findings, the capacity factor of the CHPP increased to 76%, and with the value of the cogeneration coefficient (\(k\)) close to unity, the amount of electricity generated by the CHPP was more than 67,000 MWh. Part of this electricity was used to power electric boilers that provide hot water. CHPP electricity generation on the basis of gas reciprocating plants is accompanied by CO2 emissions equal to 31.3 thousand tons.  
Figure 9: Graphic model of the Rossander graph for the selected DHS. 1—Heat delivered by thermal energy storage devices; 2—heat supply from CHPPs with their electricity transmitted to external electrical network; 3—CHPP heat stored by thermal energy storage devices with CHPP electricity supplied to external electrical network; 4—heat supply from CHPP providing electricity to consumers in DHS; 5—CHPP heat stored by thermal energy storage devices with CHP electricity supplied to consumers in DHS; 6—heat supply from CHPPs powering electric boilers; 7—CHPP heat stored by thermal energy storage devices with CHPP electricity supplied to electric boilers; 8—heat supply from electric boilers.  
A reduction in the electricity output of the TPP by 67,000 MWh, with a specific fuel consumption of 300 g.c.e./kWh, will save burned fuel by 26.2 thousand tons of coal in physical terms and cut down CO2 emissions by 70.8 thousand tons. Thus, the environmental benefit from reducing greenhouse gas emissions will be 39.5 thousand tons. A similar decrease in CO2 emissions can be achieved by generating electricity from a photovoltaic power plant with a capacity of about 27 MW, operating with a capacity factor of 16%.  
In addition to the above-discussed main benefits to be gained from the modernization of the DHS of residential consumers, there are additional benefits to be derived with further upgrading of the systems.
# Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning
###### Abstract  
In the realm of urban planning and environmental evaluation, the delineation and categorization of land types are pivotal. This study introduces a convolutional neural network-based image semantic segmentation approach to delineate parcel data in remote sensing imagery. The initial phase involved a comparative analysis of various CNN architectures. ResNet and VGG serve as the foundational networks for training, followed by a comparative assessment of the experimental outcomes. Subsequently, the VGG+U-Net model, which demonstrated superior efficacy, was chosen as the primary network. Enhancements to this model were made by integrating attention mechanisms. Specifically, three distinct attention mechanisms--spatial, SE, and channel--were incorporated into the VGG+U-Net framework, and various loss functions were evaluated and selected. The impact of these attention mechanisms, in conjunction with different loss functions, was scrutinized. This study proposes a novel network model, designated VGG+U-Net+Channel, that leverages the VGG architecture as the backbone network in conjunction with the U-Net structure and augments it with the channel attention mechanism to refine the model's performance. This refinement resulted in a 1.14% enhancement in the network's overall precision and marked improvements in MPA and MioU. A comparative analysis of the detection capabilities between the enhanced and original models was conducted, including a pixel count for each category to ascertain the extent of various semantic information. The experimental validation confirms the viability and efficacy of the proposed methodology.  
deep learning; semantic segmentation; remote sensing image; convolutional neural network; attention mechanism +
Footnote †: journal:  
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72.38 & 72.38 & 89.41 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & \(\surd\) & 87.87 & 80.65 & 91.26 \\ \hline \hline \end{tabular} \end{table}
Table 7: Improvement of basic network model and results of ablation experiment.
+++  
Figure 16: The model semantic information prediction results 2.  
Figure 14: Comparison of different models with a loss function of CE_loss, (**a**) original figure; (**b**) Channel; (**c**) SE; (**d**) Spatial; (**e**) VGG.  
Figure 15: The model semantic information prediction results 1.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 5 Conclusions  
This study established three measurement links and analyzed the peak-to-valley values, root mean square values, Gaussian curvature, positive and negative Gaussian curvature peak values, and mean square error of wavefront distortion under the influence of actual atmospheric turbulence. The following conclusions were drawn.  
1. The peak-to-valley value and root mean square value of the wavefront increase with the transmission distance. The peak-to-valley value, root mean square value, and mean square error of the wavefront distortion decrease in the order of heavy rain, dust storm, snowy, light rain, sunny, and cloudy conditions;
2. Using Gaussian curvature to describe the wavefront, it was found that the local wavefront distortion decreases in the order of snowy, heavy rain, dust storm, light rain, sunny, and cloudy conditions across the three links. In all weather conditions, more significant distortions typically occur in the central and edge regions, with the most intense changes in these areas;
3. Under the same turbulence conditions, the overall magnitude of wavefront distortion decreases as the wavelength increases, with the phase fluctuations being greater at shorter wavelengths than at longer wavelengths.  
Conceptualization, X.K. and X.H.; Data curation, X.K. and X.H.; Formal analysis, X.K.; Funding acquisition, X.K. and J.L.; Investigation, X.K.; Methodology, X.K. and X.H.; Project administration, X.H.; Resources, X.H.; Software, X.H.; Supervision, X.K.; Validation, X.K., X.H., J.L. and R.W.; Writing--original draft, X.H.; Writing--review and editing, X.K., X.H., J.L. and R.W. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China [No.61377080]; the Special Research Program of Shaanxi Provincial Department of Education [22]K0530]; the Xi'an Science and Technology Plan Project [No.23 KGDW0018-2023]; the Natural Science Basic Research Program of Shaanxi [Program No.2024]C-YBMS-557, 2024]C-YBMS-562]; Shaanxi Fundamental Science Research Project for Mathematics and Physics [Grant No.23 JSQ024]; the Key Industrial Innovation Projects in Shaanxi Province [No.2017 ZDCXL-GY-06-01]; the Xi'an Science and Technology Plan [22 GXFW0115]; and the Scientific Research Team of Xi'an University [D202309].  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to limitations of our patent work.  
The authors declare no conflicts of interest.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 2 BDCOA Based Optimum Control Signal
### Wavefront Aberration Using BDCOA  
BDCOA is used to discover optimum control signals for achieving phase compensation by minimizing the phase distortion and enhancing FSO performances. To obtain optimum control values from BDCOA, the DM obtains the phase compensation by reducing the phase distortion and enhances the FSO communications. The typical Coati Optimization Algorithm (COA) [30] is a meta heuristic algorithm that replicates the coati behavior in nature. The COA uses the principle of cooperating among agents, mimicking an animal's action. Then, the BM [31] and DMS [32] are integrated to avoid the suboptimal solutions and enhance the searching capacity, which leads to obtaining an effective balance among exploration and exploitation during the searching process. This results in enhanced global searching efficiency in optimum control signals discovery used to enhance the received signal quality. Accordingly, the minimization of phase distortion using BDCOA for DM minimizes the probability of bit errors and further improves the coupling efficiency. The searching process for optimum control signals is detailed in the following section.
## 5 Results
### Spillover Effects Analysis
#### 5.3.4 Decomposition of Spatial Spillover Effects  
The coefficient estimates from the SDM cannot be directly interpreted as marginal effects of independent variables on the dependent variable [63]. Following standard spatial econometric practices, we decompose and report the direct, indirect, and total effects for each independent variable. The direct effect captures the influence of a given independent variable on urban carbon emissions intensity within its own region, whereas the indirect effect (or spatial spillover effect) quantifies the impact of neighboring regions' characteristics on local carbon emissions intensity.  
The decomposition results from the SDM are presented in Table 9, revealing distinct mechanisms through which determinants influence urban carbon emissions intensity: (1) GPP exhibits significant decarbonization effects, with a robust direct effect of \(-\)1.360 (\(p<0.01\)) and a notable spatial spillover effect of \(-\)14.510 (\(p<0.01\)), consequently yielding an inter-regional policy multiplier of 1:10.7 (\(-\)1.360: \(-\)14.510). This means that a 1% reduction in local carbon emissions intensity caused by GPP leads to a 10.7% decline in that in neighboring cities. These effects arise from institutional synergies, such as the regional green certification mutual recognition scheme piloted in the Yangtze River Delta, coupled with technology diffusion enabled by the GPP-driven adoption of clean energy solutions. (2) Economic development exhibits a significant direct effect of \(-\)4.194 (\(p<0.01\)) and a spatial spillover effect of \(-\)4.990 (\(p<0.1\)). This implies that regional economic development has crossed the inflection point of the Kuznets curve, whereby the associated carbon emissions intensity reduction benefits of economic development have transcended geographical boundaries to generate spatial spillovers. (3) Industrial structure exhibits a significant direct effect of \(-\)2.239 (\(p<0.01\)) but indirect effect statistically insignificant at conventional levels, suggesting its effectiveness in local decarbonization but limited regional synergistic effects. (4) Urban population and total retail sales reveal complex spatial dynamics: while both exhibit negative direct effects, their positive spatial spillovers signify \"carbon leakage\" effects from population agglomeration and consumption-driven production shifts, particularly between the central city and its surrounding cities. (5) Government intervention has dual effects: a direct and spillover effect coefficient of 3.591 (\(p<0.01\)) and \(-\)20.470 (\(p<0.01\)), respectively, which indicate short-term compliance costs and regional coordination benefits of GPP. (6) Energy structure exhibits insignificant direct effects but strong positive spillovers, demonstrating a characteristic pattern of \"weak direct effects--strong spillover effects\" on carbon emission intensity. Based on the analysis above, GPP clearly influences urban carbon emissions intensity through a spatial spillover effect, which supports H4.
## 2 Theoretical Background
### Stacked Autoencoder (SAE)  
An encoder and decoder make up the unique neural network design known as the autoencoder (AE). The encoding process of raw data from the input layer to the hidden layer is given by the following equation:  
\[r=\sigma_{1}(W_{1}x+b_{1}) \tag{3}\]  
where \(x\) is the original high-dimensional input, and \(r\) is the data representation of \(x\) in the low-dimensional space. \(\sigma_{1}(W_{1}x+b_{1})\) is a nonlinear transformation of \(x\).  
The decoding process from the hidden layer to the output layer is given by the following equation:  
\[\hat{x}=\sigma_{2}(W_{2}r+b_{2}) \tag{4}\]  
where \(\hat{x}\) is the reconstruction output, and \(\sigma_{2}(W_{2}r+b_{2})\) is a nonlinear transformation of \(r\).  
By reducing reconstruction mistakes, AE reconstructs data. The formula to minimize the reconstruction error is as follows:  
\[\theta=arg\underset{\theta}{min}L(X,Z)=arg\underset{\theta}{min}\frac{1}{2} \sum_{i=1}^{N}\left\|x^{(i)}-z{(x^{(i)})}^{2}\right\| \tag{5}\]  
where X is the raw input, and Z is the output of the AE.  
SAE is obtained by first removing the output layer after training a single AE and then using \(r\) as the original input information to train a new AE. When multiple AEs are stacked, the structure of the resulting SAE is shown in Figure 2. The output of SAE can be expressed as:  
\[r_{n}=\sigma_{n}(W_{n}r_{n-1}+b_{n})\ n\geq 2 \tag{6}\]  
where \(x\) is the original high-dimensional input, \(\sigma_{n}(W_{n}r_{n-1}+b_{n})\) is a nonlinear transformation of \(r_{n-1}\), and \(r_{n  
The key to DT learning is determining how to select the optimal partition of attributes when training the DT model. Information entropy is used to divide attributes in this study. Suppose that the proportion of class \(K\) samples in the current sample set \(D\) is \(P_{k}(k=1,2,\ldots,|y|)\); the information entropy of \(D\) is defined as:  
\[Ent(D)=-\sum_{k=1}^{|y|}P_{k}\log_{2}P_{k} \tag{7}\]  
where the smaller the value of \(Ent(D)\), the higher the purity of \(D\).  
The information gain is directly based on the information entropy to calculate the change in information entropy caused by the current partition. The information gain obtained by dividing sample set \(D\) with attribute \(a\) is:  
\[Gain(D,a)=Ent(D)-\sum_{v=1}^{V}\frac{|D^{v}|}{|D|}Ent(D^{v}) \tag{8}\]  
in which the value of discrete attribute \(a\) is \(\left\{a^{1},a^{2},\ldots,a^{v},\ldots,a^{V}\right\}\), \(a^{v}\) is the \(v\)th attribute value of attribute \(a\), and there are \(V\) attribute values in attribute \(a\). The greater the information gain, the greater the purity gain obtained by using attribute \(a\) for partitioning.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 2 Related Works
This framework establishes a trusted reputation mechanism, dynamically adjusts node reputation values based on the accuracy of each training round, and uses smart contracts to ensure model accuracy and protect data privacy. Recent studies have increasingly focused on integrating metaheuristic-based approaches with edge computing systems to address challenges such as cyber attack detection, model optimization, and energy efficiency. These metaheuristic algorithms include Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Ant Colony Optimization (ACO), among others. The QMAMCC-FL [20] is designed as a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model, using a quantum Aquila optimization (QAO) algorithm, aimed at minimizing communication costs. The authors in [21] develop a blockchain-assisted hybrid metaheuristic algorithm, named BHMML-CADC, which applies Ethereum blockchain, quasi-recurrent neural network (QRNN), and the hunter-prey optimization (HPO) algorithm to build an intrusion detection system (IDS) for network attacks. Encrypting model parameters in FL tasks can effectively prevent the leakage of sensitive information through reconstruction attacks and inference attacks. [PERSON] et al. [22] utilized homomorphic encryption via the RSA algorithm to secure FL model parameters, leveraging blockchain technology for the sharing and storage of public and private keys. However, the RSA algorithm suffers from relatively slow encryption and decryption speeds, rendering it less suitable for large-scale data processing or frequent parameter updates. The aforementioned works are categorized based on their optimization directions for FL and are presented in Table 1. The proposed CS-FL comprehensively considers the problems and challenges faced by FL systems in EC scenarios, which covers all the optimization directions mentioned above and enhances the security and stability of FL systems in multiple aspects.
## 4 Discussion  
Building on the experimental results presented above, this section offers a deeper examination of the proposed method's effectiveness and limitations.  
The discussion proceeds as follows. First, focusing on the observed performance patterns among minority classes, we analyze how random patch extraction (RPE) and class-balanced sampling (CBS) produce distinct effects depending on each class's relative difficulty. Second, we comprehensively analyze the trade-off arising from changes in patch coverage, where intra-patch context and the number of patches vary inversely. Specifically, when patch coverage increases, intra-patch context grows, but the number of patches decreases, weakening the aggregation effect. Conversely, when patch coverage decreases, intra-patch context diminishes, but the number of patches increases, strengthening the aggregation effect. In this section, we examine how this trade-off impacts overall performance. Finally, we summarize the key limitations revealed by the experimental results and propose future research directions and potential expansions. By exploring these issues from multiple perspectives, we clarify why the proposed approach remains stable, even under severe class imbalance and reduced resolution; verify the impact of aggregation; and discuss its practical applicability and constraints in real-world remote sensing scenarios.
# The Presence of Marine Filamentous Fungi on a Copper-Based Antifouling Paint
###### Abstract  
Marine biofouling is undesirable growth on submerged substances, which causes a major problem for maritime industries. Antifouling paints containing toxic compounds such as copper are used to prevent marine biofouling. However, bacteria and diatoms are usually found in biofilms developed on such paints. In this study, plastic panels painted with a copper-based self-polishing antifouling paint were exposed to biofouling for 6 months in the Marina Bandar Rowdha, Sea of Oman. Clean panels were used as a control substratum. Marine filamentous fungi from protected and unprotected substrate were isolated on a potato dextrose agar. Pure isolates were identified using sequences of the ITS region of rDNA. Six fungal isolates (_Alternaria_ sp., _Aspergillus niger_, _A. terreus_, _A. tubingensis_,While antifouling pain are supposed to prevent biofouling, most of them have biofilms on their surfaces [3,13,14]. There is limited information about the biofilm composition of antifouling paints (see [15,16]). It has been shown that biofilms on antifouling paints consist of diverse species of bacteria and diatoms [17, 18, 19]. Up to now, filamentous fungi on antifouling paints have not been observed. Previous studies showed that biocides of antifouling paints and environmental conditions shaped the structure of the microbial communities [20,21].  
Marine fungi are an important component of the marine environment [22,23]. While marine filamentous fungi are not very well studied, they are widely distributed and associated with sediments, sand grains, seaweeds, submerged wood, sea animals and plants [24]. Chytidiomycota and Ascomycota are fungal divisions dominated in samples from six European near-shore sites [22]. Forty-six fungal isolates belonging to the genera _Cladosporium_, _Panphaesphaeria_, _Trichoderma_, _Alternaria_, _Phoma_, and _Arthrinium_ were isolated from marine biofilms developed on different submerged substrata [25]. Chytidiomycetes fungi dominated in marine biofilms developed on glass and plastic substrates [26]. To our knowledge, marine fungi have never been recorded on antifouling paints, especially copper-based ones. However, it was observed using metabarcoding that most of the fungal species in marine periphyton biofilms were not affected by 10 \(\upmu\)M of copper [27]. Some species of marine filamentous fungi are highly resistant to high concentrations of copper. For example, _Penicillium chrysogenum_ was able to tolerate concentrations of 500 mg L\({}^{-1}\) of copper [28].  
The main aim of this study was to identify the fungal isolates from biofilms developed on the surface of a cooper-based antifouling paint and demonstrate their copper resistance.