esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations and Experiments
### Numerical Simulations
#### 3.1.3 Error Analysis of PC Method  
Based on the error analysis in Section 2.4, we measured the error in both amplitude and phase between the diffraction distributions calculated by PS and PC methods. For each result, we accumulated and normalized the elements of the amplitude and phase matrix in columns as shown in Equation (12):  
\[\begin{split} y_{a}&=\sum_{q=1}^{Q}abs(u_{h})/max( \sum_{q=1}^{Q}abs(u_{h})),\\ y_{p}&=\sum_{q=1}^{Q}angle(u_{h})/max(\sum_{q=1}^ {Q}angle(u_{h})),\end{split} \tag{12}\]  
where \(y_{a}\) and \(y_{p}\) denote, respectively, the accumulated and normalized amplitude and phase of the diffraction field; \(u_{h}\) is the diffraction distribution; \(Q\) is the row number of the amplitude and phase matrix; and \(abs(\cdot)\) and \(angle(\cdot)\) represent the modulo and phase angle operator, respectively. Here, we take the object plane resolution of \(128\times 128\) as an example. The error in amplitude and phase between the two diffraction results is visualized by graphs as shown in Figures 8 and 9.  
With the decreasing of the curvature of SCGH, the red line and the blue line in Figure 8 tend to coincide with each other, which means the amplitude error between the PS and PC method is reducing, and it coincides well with the theoretical analysis in Section 2.4. Moreover, the error reaches the maximum when the flare angle is \(180^{\circ}\) as shown in Figures 8a and 9a. However, the phase curves in Figure 9 are relatively inconsistent. As the distance between WRP and SCGH is much greater than the wavelength, the phase is much more sensitive to the subtle difference of this distance that changes with the flare angle of WRP. In general, the error of PC method is small enough to calculate the diffraction distribution between a plane and a sphere correctly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**METHOD** & **WAVELENGTH** & **SAMPLING POINTS** & **TIME** \\ \hline PS [17] & & \(256\times 256\) & 2997.305 s \\ \hline Convolution [17] & 635 nm & 200,000 \(\times\) 200,000 & 3454.285 s \\ \hline Proposed PC & & \(256\times 256\) & 0.029 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of sampling points and calculation time required using different calculation method in visible range.
###### Abstract  
Road extraction is a key task in the field of remote sensing image processing. Existing road extraction methods primarily leverage spatial domain features of remote sensing images, often neglecting the valuable information contained in the frequency domain. Spatial domain features capture semantic information and accurate spatial details for different categories within the image, while frequency domain features are more sensitive to areas with significant gray-scale variations, such as road edges and shadows caused by tree occlusions. To fully extract and effectively fuse spatial and frequency domain features, we propose a Cross-Domain Feature Fusion Network (CDFFNet). The framework consists of three main components: the Atrous Bottleneck Pyramid Module (ABPM), the Frequency Band Feature Separator (FBFS), and the Domain Fusion Module(DFM). First, the FBFS is used to decompose image features into low-frequency and high-frequency components. These components are then integrated with shallow spatial features and deep features extracted through the ABPM. Finally, the DFM is employed to perform spatial-frequency feature selection, ensuring consistency and complementarity between the spatial and frequency domain features. The experimental results on the CHN6_CUG and Massachusetts datasets confirm the effectiveness of CDFFNet.  
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Footnote †: journal: applied sciences  
article
## 6 Conclusions and Future Perspectives
### Directions for Future Research
#### 6.3.4 Advancing Explainability  
To advance explainable AI in ocean and climate applications, future studies could incorporate methods such as SHAP, permutation feature importance, and Local Interpretable Model-agnostic Explanations (LIME) to enhance the interpretability of deep learning models. These techniques provide insights into the relative contribution of each input variable to the model's predictions, improving transparency and supporting interpretability. For example, in the context of SST prediction, a DL model can be trained using variables such as salinity, wind speed, and atmospheric pressure. SHAP can then be applied to compute Shapley values for each input feature, quantifying their individual contributions to the model's predictions. Permutation feature importance, on the other hand, evaluates the contribution of each input variable by randomly shuffling one feature at a time (e.g., wind speed) while keeping all other features unchanged. The model's performance is then re-evaluated on the perturbed dataset, and the drop in performance (e.g., an increase in RMSE) indicates the importance of the shuffled feature. Integrating these methods is beneficial for researchers to better understand model decision-making processes.
## 3 Methodology
### Loss Functions  
We train the model using a composite loss function that balances pixel-level fidelity, structural consistency, and perceptual realism:  
* L1 Loss (\(\mathcal{L}_{\text{L1}}\)): \[\mathcal{L}_{\text{L1}}=\left\|I_{\text{output}}-I_{\text{target}}\right\|_{1}\] This measures pixel-wise absolute differences.  
Figure 1: Architecture of the proposed lightweight low-light image enhancement network. It follows a UNet-inspired encoder–decoder structure with integrated channel attention modules to emphasize informative features. Skip connections help retain high-frequency details from the input image during reconstruction.  
* Structural Similarity Index (SSIM) Loss (\(\mathcal{L}_{\text{SSIM}}\)): \[\mathcal{L}_{\text{SSIM}}=1-\text{SSINM}\big{(}I_{\text{output}},I_{\text{ target}}\big{)}\] This encourages structural alignment based on luminance, contrast, and texture.
* Perceptual Loss (\(\mathcal{L}_{\text{VGG}}\)): \[\mathcal{L}_{\text{VGG}}=\sum_{l}\parallel\phi_{l}\big{(}I_{\text{output}} \big{)}-\phi_{l}\big{(}I_{\text{target}}\big{)}\parallel_{2}^{2}\] This uses feature maps \(\phi_{l}\) from a pre-trained VGG-16 network [26] to measure high-level perceptual similarity.
* Total Loss: \[\mathcal{L}_{\text{total}}=\lambda_{1}\mathcal{L}_{\text{L1}}+\lambda_{2} \mathcal{L}_{\text{SSIM}}+\lambda_{3}\mathcal{L}_{\text{VGG}}\] We use weights \(\lambda_{1}=1.0\), \(\lambda_{2}=1.0\), and \(\lambda_{3}=0.1\) by default. These values were determined empirically through ablation experiments (see Section 5.4) to achieve a balance between structural fidelity and perceptual sharpness.
# Research on Leakage Location of Pipeline Based on Module Maximum Denoising
## 2 Materials and Method
### Leakage Point Location Method
#### 2.2.2 Calculation of NPW Velocity  
The propagation speed of the NPW is related to the characteristics of fluid and the properties of pipelines. As such, the calculation formula of NPW velocity is as follows:  
\[v=\sqrt{\frac{K/\rho}{1+\frac{KD}{Ee}C_{1}}} \tag{17}\]  
In the formula, \(K\) is the bulk elastic modulus of water, Pa; \(\rho\) is the density of water, Kg/m\({}^{3}\); \(E\) is the elastic modulus of the pipe material, Pa; \(e\) is the thickness of the pipe, m; \(D\) is the inner diameter of the pipeline, m; and \(C_{1}\) is the coefficient of correction. When the pipe characteristics are known, the NPW velocity can be calculated.
## 4 Results in Practical Cases
### Results by Simulation Practical Cases  
Some MATLAB Simulink models have been designed to test the operation of the neural control of the APF in several cases, including different loads, frequency deviations, and step changes in the load.  
Both compensation objectives described in Section 2 were tested: HC and UpfC. The common objective of reducing the harmonic distortion was analyzed by measurement of the _ITHD_, which is desired to reach values under 5%, whereas the UpfC control method is expected to achieve a _PF_ close to unit, in addition to the low _ITHD_. Table 2 shows the most relevant parameters of the electric system and APF used in the simulation models. The nonlinear load consists of a set of impedance branches connected to AC regulators based on thyristors, as shown in Figure 3a.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 3 Materials and Methods
### Solar Salt  
In this analysis, the working fluid was fixed to the solar salt, made by a binary eutectic mixture composed of 60% sodium nitrate (NaNO\({}_{3}\)) and 40% potassium nitrate (KNO\({}_{3}\)). It is extensively used in concentrating solar power (CSP) plants, due to its characteristic thermal and chemical properties [26, 27]. It serves as both a heat transfer fluid (HTF) and a thermal energy storage (TES) medium, playing a critical role in enabling efficient and sustainable power generation. Solar salt circulates within CSP systems, to transfer heat from solar receivers to steam turbines, facilitating electricity generation, and it is also stored in molten salt tanks to retain excess thermal energy, allowing continuous power production during low solar radiation periods [9, 14]. This material is particularly valued for its thermal stability and operational flexibility. It remains stable across a broad temperature range of 290 \({}^{\circ}\)C to 565 \({}^{\circ}\)C, with a melting point of approximately 240 \({}^{\circ}\)C, which ensures it remains in the liquid phase under typical CSP conditions. The specific heat capacity of solar salt in the liquid phase is approximately 1.5 J/(g-K), enabling efficient thermal energy storage. Its thermal conductivity, ranging from 0.5 to 0.6 W/(m-K), supports heat transfer when paired with optimized system design. Additionally, the density of solar salt decreases linearly with temperature, from about 1900 kg/m\({}^{3}\) at 300 \({}^{\circ}\)C to 1700 kg/m\({}^{3}\) at 565 \({}^{\circ}\)C. Its low corrosivity towards common construction materials, such as stainless steel and nickel-based alloys, further enhances its suitability for CSP applications. For the numerical simulations conducted in this study, two critical properties were required: the density and the speed of sound. It is essential to distinguish these properties between the liquid and solid phases of solar salt, as they significantly influence the propagation of ultrasonic waves. The following relationships were used to represent these properties [26, 27, 28]:* **Liquid phase:** \[\rho =2090-0.636T,\] (9) \[c =2478-1.150T,\] (10) where \(T\) is the temperature expressed in \(K\).
* **Solid phase:** \[\rho =2100\;\mathrm{kg/m^{3}},\] (11) \[c =3000\;\mathrm{m/s}.\] (12)  
This distinction allowed the simulations to accurately capture the acoustic behavior of solar salt under different thermal and phase conditions, enabling the assessment of ultrasonic detection methods for identifying solid inclusions.
## 4 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hour**} & \multicolumn{3}{c}{**Fundamental**} & \multicolumn{3}{c}{**Traditional**} & \multicolumn{3}{c}{**IEEE Standard C57.110-2018 \(P_{cc,Hz}\)**} \\  & \multicolumn{3}{c}{\(P_{cc,12}\) **(W)**} & \multicolumn{3}{c}{**\(P_{ccN,Hz}\) (W)**} & \multicolumn{3}{c}{**(W)**} \\ \cline{2-10}  & **A-Phase** & **B-Phase** & **C-Phase** & **A-Phase** & **B-Phase** & **C-Phase** & **A-Phase** & **B-Phase** & **C-Phase** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 12: Load losses due to the non-fundamental currents according to traditional methods and based on IEEE Standard C57.110-2018 procedures, respectively, and relative differences on 10 November 2022. (4) The values of load losses due to the exclusive effect of harmonics calculated based on IEEE Standard C57.110-2018 (\(P_{cc,Hz}\)) are always much higher (Table 12) than those calculated with the traditional method (\(P_{ccN,Hz}\)). The traditional method underestimates the effects of harmonics on load losses compared to the procedure based on IEEE Standard C57.110-2018. This is observed in Figures 1-3, which represent the harmonic loss factors (\(HL_{z}\%\)) of phases A, B, and C, respectively, obtained throughout 10 November 2022, applying Equation (30), using the load loss values determined by IEEE Standard C57.110-2018 and the traditional method, respectively. In these figures, the following statements are verified:  
(1) The importance of non-fundamental harmonics in the load losses of each phase of the transformer, determined by the factor \(HL_{z}\%\), is much greater using IEEE Standard C57.110-2018 than the traditional method. As an example, it is observed in Figure 3 that the load losses due to non-fundamental frequency harmonics calculated at 6:55 a.m. on 10 November 2022 (time interval No. 6) are 55.57% of the load losses in phase C, according to the Standard. However, these load losses only reach 13.3% of the load losses in phase C, using the traditional method. (2) The harmonic loss factors (\(HL_{z}\%\)) in both procedures, the IEEE Standard C57.110-2018 and the traditional method, increase their values with the current distortion rates (\(THDi_{z}\%\)), which, in this application example, usually coincide with the hours of lowest consumption in homes (Figures 1-3). (3) Based on the information presented above, we can conclude that household loads are much less distorted than the rest of the loads connected to the residential distribution network. That is because in the hours of highest household consumption, the harmonic loss factors have their lowest values (Figures 1-3). Figure 1: Transformer’s A-phase harmonic loss factor (\(HL_{A}\%\)) based on IEEE Standard C57.110-2018 and traditional procedures, and A-phase total harmonic distortion (\(THDi_{A}\%\)) on 10 November 2022.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 5 Finite Element Modeling Investigation  
### Finite Element Model Description  
In order to study the mechanical properties of multi-cavity steel-concrete composite floor, and the influence of steel thickness, composite floor thickness and span on the bearing capacity of composite floor, ANSYS software was used for finite element simulation.  
Element selection: In the model, SOLID65 is used as the concrete element, and SHELL181 with large deformation and nonlinear function is used for the multi-cavity steel plate element [27].  
Material parameters: It was assumed that the concrete was ideally homogeneous with the strength grade of C30, the elastic modulus of \(3\times 10^{4}\) MPa and the Poisson's ratio was 0.2. The steel strength grade was Q235, the elastic modulus was \(2.06\times 10^{5}\) MPa and the Poisson's ratio was 0.3. The steel thickness was 1 mm, the specimen thickness was 120 mm, the cavity size was 100 mm, the floor span was 2500 mm and the floor width was 1000 mm. W-W five parameter yield criterion was used for concrete [28; 29], and Mises yield criterion was used for the stress-strain relationship of steel.  
Constraint conditions: In order to make the simulation results close to the test results, according to the test that the composite floor is directly placed on the bottom adminiculum, no lateral or vertical movement occurred during the loading process, so the displacement constraints in Y direction and Z direction were applied to the model, and the displacement in both directions was zero; the displacement in X direction was set as zero at the longitudinal end of the composite floor.  
Load application: According to the test procedure, the concentrated load was converted into linear load in the simulation, which was applied at the third point of the upper surface of the composite floor.  
Composite floor model: The composite floor model consists of a multi-cavity steel plate model and a concrete model. The concrete was a solid unit, and the cavity steel plate framework was embedded into the concrete to form the composite floor.  
It can be observed that the concrete and the cavity-steel plate were well bonded, and the cavity steel plate was not damaged by the concrete extrusion when the concrete was under compression, so the relative movement of the concrete and the cavity steel plate at the interface was not considered in the model. Because the cavity steel plate opening has no effect on the elastic modulus of steel, in order to simplify the model, there was no hole in the cavity steel plate during the modeling. In order to ensure the unity of the simulation and test, the model size was the same as the test [30; 31]. The composite floor model is shown in Figure 13.
## 4 Results and Discussion
### Indoor Experimental Results of High-Resolution SAR Imaging
Figure 9: The SAR experimental imaging results of satellite model target with different Q factors: (**a**) Nyquist real-time sampling; (**b**) \(Q=2\), \(t_{\text{hs}}=0.66\) s; (**c**) \(Q=6/5\), \(t_{\text{hs}}=1.09\) s; (**d**) \(Q=2/3\), \(t_{\text{hs}}=1.97\) s.  
Moreover, to quantitatively analyze the imaging performance, the sharpness metric parameter (\(S\)) is quoted to predict the relative amount of blurriness in images [22], which is defined as the ratio:  
\[S=\frac{\sum_{(x,y)}\left|\sigma(x,y)\right|^{4}}{\left|\sum_{(x,y)}\left|\sigma( x,y)\right|^{2}\right|^{2}} \tag{10}\]  
where \(\sigma(x,\,y)\) is the amplitude of the complex image at point (\(x,\,y\)). When the image becomes blurry, the value of the sharpness metric decreases. According to Equation (10), the sharpness metric for Figure 9a-d is calculated to be \(1.436\times 10^{-6},1.433\times 10^{-6},1.416\times 10^{-6}\) and \(1.387\times 10^{-6}\), respectively. Compared to Figure 9a, the value of the sharpness metric for Figure 9c to Figure 9d is decreased \(0.21\%\), \(1.39\%\) and \(3.41\%\), respectively. The calculated results are consistent with the visual perception of images, and it shows that the imaging performance decreases with the decrease in the value of the sharpness metric.
# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
## 4 Multi-Sensor and Multi-Layer Fault Recognition Based on LPP
### Locality-Preserving Projection
The LPP algorithm assumes that manifolds are linear, so  
\[y_{i}=K^{T}x_{i} \tag{14}\]  
By bringing Formulas (13) and (14) into Formula (11) for calculation, it can be concluded that:  
\[\sum\limits_{i,j=0}^{n}{\left\|y_{i}-y_{j}\right\|^{2}W_{ij}}=2K^{T}XLX^{T}K \tag{15}\]  
According to the constraints in the Laplacian feature mapping, it is obtained that:  
\[K^{T}XDX^{T}K=1 \tag{16}\]Therefore, the objective function of LPP is as follows:  
\[\text{argmin}_{K^{T}XDX^{T}K}K^{T}XLX^{T}K \tag{17}\]  
Therefore, the most-mapped vector of the LPP algorithm can be obtained by finding the eigenvalue and eigenvector of the following formula:  
\[XLX^{T}K=\lambda DX^{T}K \tag{18}\]  
where \(\lambda\) is the eigenvalue and \(K\) represents the corresponding eigenvector. The first \(d\) eigenvectors from \(k_{1},k_{2},\ldots,k_{l}\) are associated with the \(l\) smallest eigenvalue. Finally, the dimensionality reduction is as follows:  
\[Y=K^{T}X\ K=[k_{1},k_{2},\ldots,k_{d}] \tag{19}\]  
where \(K\) is a \(D\times d\) projection matrix. A reduced d-dimensional vector \(y_{i}\) is received after the D-dimensional input \(x_{i}\) is projected. The reduced vector \(Y\) preserves the local variation information in regard to the relationship with its neighbors and will serve as the new feature. Therefore, LPP is able to seek the optimal linear locality structure embedded in that matrix, and the locality-preserving characteristic makes it relatively insensitive to isolated values and noise.
## 6 Conclusions  
A review of current and prospective DCN technologies to accommodate the aggregate capacity demands has been provided. We demonstrated a 320 Gbps (32 \(\times\) 10 Gbps) bidirectional WDM-FSO access system for use in DCNs, where data speeds are typically high and air turbulence is minimal. The wavelength reuse scheme based on wavelength-seeded RSOAs has been employed to decrease the cost of WDM-FSO topologies. The WDM-FSO scheme was implemented with a centralized multi-wavelength comb source and direct detection. An OFDM-modulated signal has been transmitted over the downlink. For the uplink, the downstream OFDM signal was re-modulated as an OOK signal by an RSOA at the receiver. BER measurements of FSO signals show that the proposed WDM-FSO system may achieve bidirectional 10 Gbps of traffic for each wavelength over a 1000 m free space transmission length. The BER results show that the proposed high data rate FSO topology offers an excellent alternative to fiber-based optical interconnects in DCNs or point-to-point links in PONs, allowing for high data rate bidirectional transmission.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Input Facet Reflectivity & \(50\times 10^{-6}\) \\ \hline Output Facet Reflectivity & \(50\times 10^{-6}\) \\ \hline Active Length & 0.0006 m \\ \hline Taper Length & 0.0001 m \\ \hline Width & \(0.4\times 10^{-6}\) m \\ \hline Height & \(0.4\times 10^{-6}\) m \\ \hline Optical Confinement Factor & 0.45 \\ \hline Nonlinear Gain Parameter & \(112\times 10^{-6}\) m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main RSOA parameters.  
Figure 9: BER versus Link range plots for uplink.  
**Author Contributions:** Conceptualization, F.E.-N.; methodology, F.E.-N., T.X., D.A. and M.L.; software, F.E.-N.; validation, F.E.-N., T.X., D.A. and M.L.; formal analysis, F.E.-N.; investigation, F.E.-N.; resources, F.E.-N. and T.X; data curation, F.E.-N.; writing--original draft preparation, F.E.-N. and D.A; writing--review and editing, T.X. and M.L.; visualization, F.E.-N.; supervision, M.L.; project administration, F.E.-N., T.X., D.A. and M.L.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by EU Horizon 2020 MSCA Grant (No. 101008280).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background and Related Work
### Overview of SDN  
The emergence of the software-defined network (SDN) paradigm has attracted great interest in designing intelligent, flexible. and programmable networks. As defined by the Open Networking Foundation (ONF), SDN refers to an emerging network architecture, where network control policies are decoupled from the forwarding mechanism and are directly programmable [11]. Unlike traditional networks that are vertically integrated, the control and data planes are decoupled in SDN frameworks. As a result, control logic and network intelligence are moved to an external entity called the SDN controller, while network devices simply make forwarding decisions that are flow-based rather than destination-based [12]. The network is programmable through software applications running on top of the SDN controllers that logically control the underlying network infrastructure and interact with the upper-layer management panel.  
With its inherent characteristics of decoupling the control and data panels and programmability on the centralized control panel, SDN brings potential benefits in conventional network architecture and operations [11]. SDN can enhance network configuration and management by using the unification of the control panel over heterogeneous network devices; thus, the entire network can be easily configured with programmable controllers and then dynamically optimized according to the global network status. In addition, an SDN controller allows for the centralization of the control logic with global knowledge of the network state, and it is promising to improve network performance with optimal utilization of the underlying infrastructure. Moreover, SDN offers a convenient platform for the validation of techniques and encourages the innovation of next-generation networks.